WO2020173152A1

WO2020173152A1 - Facial appearance prediction method and electronic device

Info

Publication number: WO2020173152A1
Application number: PCT/CN2019/120085
Authority: WO
Inventors: 董继阳
Original assignee: 华为技术有限公司
Priority date: 2019-02-26
Filing date: 2019-11-22
Publication date: 2020-09-03
Also published as: CN109994206A

Abstract

Provided are a facial appearance prediction method and an electronic device, related to the technical field of image processing, allowing a vivid simulation of changes in the facial appearance of a user on the basis of lifestyle habits of the user, enabling the user to intuitively perceive the changes in the facial appearance, thus reminding the user to adjust poor lifestyle habits in a timely manner. The method comprises: an electronic device acquires a first image, the first image comprising a first facial image of a user; the electronic device acquires health data of the user in a preset time, the health data comprising at least one of exercise data, sleep data, nutritional intake data, or data on the length of time that the user spends on using an electronic device; the electronic device makes a prediction with respect to the facial appearance of the user on the basis of the health data and of the first facial image to produce a second image, the second image comprising a second facial image of the user; and the electronic device displays a first interface, the first interface comprising the second facial image, or, the first interface comprising the first facial image and the second facial image.

Description

Appearance prediction method and electronic equipment

This application claims the priority of a Chinese patent application filed with the State Intellectual Property Office of China, the application number is 201910142345.3, and the invention title is "a method and electronic device for facial appearance prediction" on February 26, 2018. The entire content is incorporated by reference in In this application.

Technical field

This application relates to the field of image processing technology, and in particular to a facial prediction method and electronic equipment.

Background technique

With the rapid development of image processing technology, currently some service providers can provide users with the function of aging or de-aging the user's facial images.

For example, some Meitu applications (APP) can beautify the wrinkles, spots, and acne that appear in the user's facial image, and make the beautified user's facial image younger. For another example, some applications provide the function of predicting the facial image of the user's appearance after aging. This type of application can obtain a photo containing an image of the user's face, and further, the application can use an aging algorithm to age the facial image according to the passage of time, so as to obtain an image of the user's facial aging after a certain period of time (for example, 10 years, 20 years). However, there are many factors that affect the appearance of users, and it is not accurate to predict the appearance of users based on the passage of time, and users cannot know the cause of appearance aging in time.

Summary of the invention

The present application provides a facial appearance prediction method and electronic device that can truly simulate the changes in the user's facial appearance based on the user's living habits, so that the user can intuitively feel the changes in the facial appearance, thereby reminding the user to adjust bad living habits in time.

In order to achieve the above objectives, this application adopts the following technical solutions:

A first aspect provides a face prediction method, including: an electronic device acquires a first image, the first image includes a first facial image of a user, of course, the first image may also include a bust or a bust containing the first facial image. Full-length images, etc.; and, the electronic device may obtain the user's health data within a preset time. For example, the health data may include at least one of the user's exercise data, sleep data, nutritional intake data, or time data of using the electronic device In this way, based on the aforementioned health data and the first facial image, the electronic device can predict the appearance of the user to obtain the predicted second image, which can include the user's second facial image; further, the electronic device can The first interface is displayed, and the first interface includes the predicted second facial image. Of course, the first interface may also include the first facial image of the user before the prediction.

It can be seen that the appearance change predicted by the electronic device for the user can change with the user's health data, that is, the appearance change predicted by the electronic device for the user is closely related to the user's actual living habits. In this way, the electronic device not only considers time-lapse factors when predicting the user’s appearance for the user, but also combines the user’s health data to make the prediction result of the user’s appearance more accurate, so that the user can intuitively feel the change in the appearance, thereby Remind users to timely adjust bad living habits or maintain good living habits.

In a possible implementation manner, before the electronic device acquires the above-mentioned first image, it further includes: the electronic device displays a second interface of the prediction application, and the second interface includes a button for the facial prediction function; at this time, the electronic device acquires the first image. An image specifically includes: in response to the user clicking the button, the electronic device can use a camera to obtain the first image; or, in response to the user clicking the button, the electronic device can obtain a photo from the album application as the above The first image.

Alternatively, before the electronic device acquires the first image, the method further includes: the electronic device may determine that the health data meets a preset condition, for example, the preset condition includes that the health data is greater than a preset value, or the health data is less than a preset value . When the above health data is less than the preset value, it indicates that the user has some bad lifestyle habits. At this time, the electronic device can automatically obtain the user's first facial image, and combine the user's health data to predict the user's appearance, thereby reminding the user in time Adjust bad habits. When the above health data is greater than the preset value, it indicates that the user has good living habits. At this time, the electronic device can automatically obtain the user's first facial image, and combine the user's health data to predict the user's appearance, thereby reminding the user to maintain good health Life habits.

In a possible implementation manner, after the electronic device predicts the user's appearance based on the above-mentioned health data and the first facial image, and obtains the second image, the method further includes: the electronic device displays a notification message that the appearance prediction is completed; The device displaying the first interface includes: in response to the user's operation of opening the notification message, the electronic device opens the prediction application and displays the first interface of the prediction application.

In a possible implementation manner, the electronic device predicts the user's appearance based on the aforementioned health data and the first facial image to obtain the second image, including: the electronic device determines the corresponding age influence value K according to the aforementioned health data, and The age influence value refers to the deviation value that is positively or negatively correlated with the user’s current age; generally, when the user’s health data reflects the healthier living habits, the age influence value K is a negative value. When the user’s health data reflects When the living habits are more unhealthy, the age influence value K is positive; further, the electronic device can predict the second facial image of the user in M+K years based on the first facial image, and obtain a second image containing the second facial image. Among them, M is the default value or the value set by the user. In this way, the predicted second facial image can not only reflect the change of the user's face with age, but also reflect the influence of the user's current living habits on the user's appearance, thereby providing the accuracy of appearance prediction and the user's experience.

In a possible implementation manner, the electronic device predicts the user's appearance based on the aforementioned health data and the first facial image to obtain the second image, including: the electronic device predicts the user's first facial image in M years based on the aforementioned first facial image Three facial images, where M is a default value or a value set by the user; further, the electronic device can add a corresponding facial effect to the third facial image according to the above-mentioned health data to obtain the second facial image of the user after M years. For example, when the above-mentioned health data reflects the user's bad living habits, the corresponding one can be added; when the above-mentioned health data reflects the user's healthy living habits, the corresponding youthful effect can be added.

In a possible implementation manner, the above-mentioned first interface includes a second facial image and a first switch button; wherein, after the electronic device displays the first interface, it further includes: in response to an operation of the user clicking the first switch button, The electronic device switches the displayed second facial image to the first facial image.

In a possible implementation manner, the above-mentioned first interface includes a second facial image and a second switch button; wherein, after the electronic device displays the first interface, it further includes: in response to an operation of the user clicking the second switch button, The electronic device switches the displayed second image to a standard facial image, the standard facial image corresponding to preset standard health data. In this way, the user can intuitively know the impact of the current lifestyle and healthy lifestyle on the future appearance by comparing the two images, thereby reminding and urging the user to establish a healthier lifestyle.

In a possible implementation manner, displaying the first interface by the electronic device specifically includes: the electronic device marks the appearance change corresponding to the above-mentioned health data in the second facial image on the first interface. In this way, the user can intuitively understand how the current bad life habits will specifically affect the appearance, so as to remind the user to adjust the bad life habits in time.

In a possible implementation manner, the above-mentioned first interface may also include methods or suggestions for adjusting the user's living habits, so as to help and guide the user to adjust bad living habits as soon as possible.

In a possible implementation, the above-mentioned first interface may also include an aging progress bar and a slider; the second facial image includes a first predicted image and a second predicted image of the user's face; wherein, the electronic device is in the first interface Displaying the second facial image in, includes: if it is detected that the slider is dragged to the first position of the aging progress bar, the electronic device may display the first predicted image corresponding to the first position, and the first predicted image is the predicted The user’s facial image after the first time period; if it is detected that the slider is dragged to the second position of the aging progress bar, the electronic device can display a second predicted image corresponding to the second position, and the second predicted image is a predicted The facial image of the user after the second period of time. In other words, the electronic device can display facial images predicted for the user based on the current user’s living habits at different times in chronological order, so that the user can dynamically feel the changes in facial appearance over time while maintaining the current living habits .

In a possible implementation manner, the above-mentioned second image may also include a body shape template of the user predicted for the user after a period of time, the body shape template corresponding to its health data, and the body shape template may include a fattening template or a thinning template. In this way, the user can intuitively and vividly understand the influence of his own life habits on his body shape in the future, thereby reminding the user to adjust bad life habits in time.

In a possible implementation, the electronic device acquiring the user's health data within a preset time includes: the electronic device acquires the user's health data within the preset time from the wearable device.

A second aspect provides a facial appearance prediction method, including: an electronic device acquires health data of a user within a preset time. The health data may include the user's exercise data, sleep data, nutritional intake data, or data on the duration of using the electronic device At least one of; and the electronic device may obtain a first image, and the first image includes a first facial image of the user. For example, the first image may be obtained by the electronic device using a camera, or the first image may be The electronic device obtains a photo from the photo album application; if the above health data does not meet the preset conditions, indicating that the user’s health data has bad lifestyle habits, the electronic device can display the first facial image and The second facial image predicted by the user after a period of time. The second facial image is the predicted image after the first facial image is aged; accordingly, if the above-mentioned health data meets the preset condition, it indicates that the user’s living habits are relatively healthy. Then, the electronic device may display the first facial image and the third facial image predicted for the user after a period of time in one interface, and the third facial image is a predicted image after the first facial image is de-aging.

In a possible implementation, if the aforementioned health data does not meet the preset condition, the aforementioned interface may further include a first figure template carrying a first facial image and a second figure template carrying a second facial image. The second body shape template is the result of the first body shape template becoming fat; if the above-mentioned health data meets the preset conditions, the second interface may also include a first body shape template carrying the first facial image and the third facial image The third body shape template is the result of the first body shape template becoming thinner. In this way, the user can intuitively and vividly understand the influence of his own life habits on his body shape in the future, thereby reminding the user to adjust bad life habits in time.

In a possible implementation manner, the foregoing period of time is M years, and M is the default value or a value set by the user; when the foregoing health data does not meet the preset conditions, it further includes: the electronic device is based on the foregoing health data and M years, Perform aging processing on the first facial image to obtain the second facial image of the user after M years; correspondingly, when the above-mentioned health data meets the preset conditions, it also includes: the electronic device performs the correction of the first facial image based on the above-mentioned health data and M years A facial image is subjected to aging processing to obtain the third facial image of the user after M years.

In a possible implementation manner, if the aforementioned health data does not meet the preset condition, the electronic device may also display a notification message that the appearance prediction is completed. If it is detected that the user has opened the notification message, the electronic device may open the prediction application and display the aforementioned second facial image.

In a possible implementation manner, when the electronic device displays the above-mentioned second facial image, it may also mark changes in appearance corresponding to the above-mentioned health data in the second facial image. In this way, the user can intuitively understand how the current bad life habits will specifically affect the appearance, so as to remind the user to adjust the bad life habits in time.

In a possible implementation manner, when the electronic device displays the above-mentioned second facial image, it may also display methods or suggestions for adjusting the user's living habits, so as to help and guide the user to adjust bad living habits as soon as possible.

A third aspect provides an electronic device, including: a touch screen, one or more processors, one or more memories, and one or more computer programs; wherein the processor is coupled with the touch screen and the memory, and one or more of the foregoing The computer program is stored in the memory, and when the electronic device is running, the processor executes one or more computer programs stored in the memory, so that the electronic device executes any one of the aforementioned facial prediction methods.

A fourth aspect provides a computer storage medium, including computer instructions, which when the computer instructions run on an electronic device, cause the electronic device to execute any one of the aforementioned facial prediction methods.

A fifth aspect provides a computer program product, which when the computer program product runs on an electronic device, causes the electronic device to execute any one of the aforementioned facial prediction methods.

A sixth aspect provides a graphical user interface (GUI), which is stored in the above-mentioned electronic device. The electronic device includes a touch screen, a memory, and a processor, and the processor is configured to execute the One or more computer programs in the memory, the graphical user interface may include: a first GUI displayed on the touch screen, and the first GUI includes a button with a facial appearance prediction function; in response to a touch event directed to the button, on the touch screen Display the second GUI, the second GUI includes the user's first facial image and the second facial image, or the second GUI includes the user's second facial image; where the first facial image is the user's real facial image The second facial image is a facial image that the electronic device predicts for the user after a period of time based on the user's health data and the first facial image.

A seventh aspect provides a GUI, the GUI is stored in the above electronic device, the electronic device includes a touch screen, a memory, and a processor, and the processor is configured to execute one or more computer programs stored in the memory. The graphical user interface may include: a first GUI displayed on the touch screen, the first GUI including a notification message that the appearance prediction is completed; in response to a touch event for the above notification message, a second GUI is displayed on the touch screen, and the second GUI Including the user’s first facial image and the second facial image, or, the second GUI includes the user’s second facial image; where the first facial image is the real facial image of the user, and the second facial image is based on the electronic device The user's health data and the first facial image are the facial images predicted by the user after a period of time.

Understandably, the electronic equipment described in the third aspect, the computer storage medium described in the fourth aspect, the computer program product described in the fifth aspect, and the GUI described in the sixth and seventh aspects provided above are all used for The corresponding method provided above is executed. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the corresponding method provided above, which will not be repeated here.

Description of the drawings

FIG. 1 is a first structural diagram of an electronic device according to an embodiment of the application;

2 is a schematic diagram of a photographing principle of a camera provided by an embodiment of the application;

FIG. 3 is a schematic diagram 1 of an application scenario of a face prediction method provided by an embodiment of this application;

FIG. 4 is a first schematic flowchart of a face prediction method provided by an embodiment of the application;

FIG. 5 is a second schematic diagram of an application scenario of a face prediction method provided by an embodiment of the application;

FIG. 6 is a schematic diagram 3 of an application scenario of a face prediction method provided by an embodiment of this application;

FIG. 7 is a fourth schematic diagram of an application scenario of a face prediction method provided by an embodiment of this application;

FIG. 8 is a schematic diagram 5 of an application scenario of a face prediction method provided by an embodiment of this application;

FIG. 9 is a sixth schematic diagram of an application scenario of a face prediction method provided by an embodiment of this application;

10 is a schematic diagram 7 of an application scenario of a face prediction method provided by an embodiment of this application;

FIG. 11 is a schematic diagram eight of an application scenario of a face prediction method provided by an embodiment of this application;

FIG. 12 is a schematic diagram 9 of an application scenario of a face prediction method provided by an embodiment of this application;

FIG. 13A is a tenth schematic diagram of an application scenario of a face prediction method provided by an embodiment of this application;

13B is a schematic diagram eleventh of an application scenario of a face prediction method provided by an embodiment of this application;

FIG. 14 is a twelfth schematic diagram of an application scenario of a face prediction method provided by an embodiment of this application;

15 is a thirteenth schematic diagram of an application scenario of a face prediction method provided by an embodiment of this application;

16 is a fourteenth schematic diagram of an application scenario of a face prediction method provided by an embodiment of this application;

FIG. 17 is a schematic diagram 15 of an application scenario of a face prediction method provided by an embodiment of this application;

18 is a sixteenth schematic diagram of an application scenario of a face prediction method provided by an embodiment of this application;

FIG. 19 is a seventeenth schematic diagram of an application scenario of a face prediction method provided by an embodiment of this application;

20 is a schematic diagram eighteenth of an application scenario of a face prediction method provided by an embodiment of this application;

FIG. 21 is a second schematic flowchart of a facial appearance prediction method provided by an embodiment of this application;

FIG. 22 is a second structural diagram of an electronic device provided by an embodiment of this application.

detailed description

The implementation of this embodiment will be described in detail below with reference to the accompanying drawings.

Exemplarily, the appearance prediction method provided by the embodiments of the present application can be applied to mobile phones, tablet computers, notebook computers, ultra-mobile personal computers (UMPC), handheld computers, netbooks, and personal digital assistants (personal digital assistants). Digital assistant (PDA), wearable electronic devices, virtual reality devices, and other electronic devices, the embodiments of the present application do not make any restrictions on this.

Taking the mobile phone 100 as an example of the above electronic device, FIG. 1 shows a schematic structural diagram of the mobile phone 100.

The mobile phone 100 may include a processor 110, an external memory interface 120, an internal memory 121, a universal serial bus (USB) interface 130, a charging management module 140, a power management module 141, a battery 142, an antenna 1, an antenna 2, RF module 150, communication module 160, audio module 170, speaker 170A, receiver 170B, microphone 170C, earphone interface 170D, sensor module 180, buttons 190, motor 191, indicator 192, camera 193, display 194, and user identification module (subscriber identification module, SIM) card interface 195, etc.

It is understandable that the structure illustrated in the embodiment of the present application does not constitute a specific limitation on the mobile phone 100. In other embodiments of the present application, the mobile phone 100 may include more or fewer components than shown, or combine certain components, or split certain components, or arrange different components. The illustrated components can be implemented in hardware, software, or a combination of software and hardware.

The processor 110 may include one or more processing units. For example, the processor 110 may include an application processor (AP), a modem processor, a graphics processing unit (GPU), and an image signal processor. (image signal processor, ISP), controller, memory, video codec, digital signal processor (digital signal processor, DSP), baseband processor, and/or neural-network processing unit (NPU) Wait. Among them, the different processing units may be independent devices or integrated in one or more processors.

The controller may be the nerve center and command center of the mobile phone 100. The controller can generate operation control signals according to the instruction operation code and timing signals to complete the control of fetching and executing instructions.

A memory may also be provided in the processor 110 to store instructions and data. In some embodiments, the memory in the processor 110 is a cache memory. The memory can store instructions or data that have just been used or recycled by the processor 110. If the processor 110 needs to use the instruction or data again, it can be directly called from the memory. Repeated accesses are avoided, the waiting time of the processor 110 is reduced, and the efficiency of the system is improved.

In some embodiments, the processor 110 may include one or more interfaces. The interface may include an integrated circuit (inter-integrated circuit, I2C) interface, an integrated circuit built-in audio (inter-integrated circuit sound, I2S) interface, a pulse code modulation (pulse code modulation, PCM) interface, and a universal asynchronous transmitter (universal asynchronous transmitter) interface. receiver/transmitter, UART) interface, mobile industry processor interface (MIPI), general-purpose input/output (GPIO) interface, subscriber identity module (SIM) interface, and / Or Universal Serial Bus (USB) interface, etc.

The I2C interface is a two-way synchronous serial bus, which includes a serial data line (SDA) and a derail clock line (SCL). In some embodiments, the processor 110 may include multiple sets of I2C buses. The processor 110 may be coupled to the touch sensor 180K, charger, flash, camera 193, etc. through different I2C bus interfaces. For example, the processor 110 may couple the touch sensor 180K through an I2C interface, so that the processor 110 and the touch sensor 180K communicate through the I2C bus interface to realize the touch function of the mobile phone 100.

The I2S interface can be used for audio communication. In some embodiments, the processor 110 may include multiple sets of I2S buses. The processor 110 may be coupled with the audio module 170 through an I2S bus to realize communication between the processor 110 and the audio module 170. In some embodiments, the audio module 170 may transmit audio signals to the communication module 160 through an I2S interface, so as to realize the function of answering calls through a Bluetooth headset.

The PCM interface can also be used for audio communication to sample, quantize and encode analog signals. In some embodiments, the audio module 170 and the communication module 160 may be coupled through a PCM bus interface. In some embodiments, the audio module 170 may also transmit audio signals to the communication module 160 through the PCM interface, so as to realize the function of answering calls through the Bluetooth headset. Both the I2S interface and the PCM interface can be used for audio communication.

The UART interface is a universal serial data bus used for asynchronous communication. The bus can be a two-way communication bus. It converts the data to be transmitted between serial communication and parallel communication. In some embodiments, the UART interface is generally used to connect the processor 110 and the communication module 160. For example, the processor 110 communicates with the Bluetooth module in the communication module 160 through the UART interface to realize the Bluetooth function. In some embodiments, the audio module 170 may transmit audio signals to the communication module 160 through a UART interface, so as to realize the function of playing music through a Bluetooth headset.

The MIPI interface can be used to connect the processor 110 with the display screen 194, the camera 193 and other peripheral devices. The MIPI interface includes camera serial interface (camera serial interface, CSI), display serial interface (display serial interface, DSI), etc. In some embodiments, the processor 110 and the camera 193 communicate through a CSI interface to implement the shooting function of the mobile phone 100. The processor 110 and the display screen 194 communicate through a DSI interface to realize the display function of the mobile phone 100.

The GPIO interface can be configured through software. The GPIO interface can be configured as a control signal or as a data signal. In some embodiments, the GPIO interface may be used to connect the processor 110 with the camera 193, the display screen 194, the communication module 160, the audio module 170, the sensor module 180, and so on. GPIO interface can also be configured as I2C interface, I2S interface, UART interface, MIPI interface, etc.

The USB interface 130 is an interface that complies with the USB standard specification, and specifically may be a Mini USB interface, a Micro USB interface, a USB Type C interface, and so on. The USB interface 130 can be used to connect a charger to charge the mobile phone 100, and can also be used to transfer data between the mobile phone 100 and peripheral devices. It can also be used to connect headphones and play audio through the headphones. This interface can also be used to connect other electronic devices, such as AR devices.

It can be understood that the interface connection relationship between the modules illustrated in the embodiment of the present application is merely a schematic description, and does not constitute a structural limitation of the mobile phone 100. In other embodiments of the present application, the mobile phone 100 may also adopt different interface connection modes in the foregoing embodiments, or a combination of multiple interface connection modes.

The charging management module 140 is used to receive charging input from the charger. Among them, the charger can be a wireless charger or a wired charger. In some wired charging embodiments, the charging management module 140 may receive the charging input of the wired charger through the USB interface 130. In some wireless charging embodiments, the charging management module 140 may receive the wireless charging input through the wireless charging coil of the mobile phone 100. While the charging management module 140 charges the battery 142, it can also supply power to the electronic device through the power management module 141.

The power management module 141 is used to connect the battery 142, the charging management module 140 and the processor 110. The power management module 141 receives input from the battery 142 and/or the charge management module 140, and supplies power to the processor 110, the internal memory 121, the external memory, the display screen 194, the camera 193, and the communication module 160. The power management module 141 can also be used to monitor parameters such as battery capacity, battery cycle times, and battery health status (leakage, impedance). In some other embodiments, the power management module 141 may also be provided in the processor 110. In other embodiments, the power management module 141 and the charging management module 140 may also be provided in the same device.

The wireless communication function of the mobile phone 100 can be implemented by the antenna 1, the antenna 2, the radio frequency module 150, the communication module 160, the modem processor, and the baseband processor.

The antenna 1 and the antenna 2 are used to transmit and receive electromagnetic wave signals. Each antenna in the mobile phone 100 can be used to cover a single or multiple communication frequency bands. Different antennas can also be reused to improve antenna utilization. For example, antenna 1 can be multiplexed as a diversity antenna of a wireless local area network. In other embodiments, the antenna can be used in combination with a tuning switch.

The radio frequency module 150 may provide a wireless communication solution including 2G/3G/4G/5G and the like applied to the mobile phone 100. The radio frequency module 150 may include at least one filter, switch, power amplifier, low noise amplifier (LNA), etc. The radio frequency module 150 can receive electromagnetic waves from the antenna 1, filter and amplify the received electromagnetic waves, and transmit them to the modem processor for demodulation. The radio frequency module 150 can also amplify the signal modulated by the modem processor, and convert it into electromagnetic wave radiation via the antenna 1. In some embodiments, at least part of the functional modules of the radio frequency module 150 may be provided in the processor 110. In some embodiments, at least part of the functional modules of the radio frequency module 150 and at least part of the modules of the processor 110 may be provided in the same device.

The modem processor may include a modulator and a demodulator. Among them, the modulator is used to modulate the low frequency baseband signal to be sent into a medium and high frequency signal. The demodulator is used to demodulate the received electromagnetic wave signal into a low-frequency baseband signal. Then the demodulator transmits the demodulated low-frequency baseband signal to the baseband processor for processing. The low-frequency baseband signal is processed by the baseband processor and then passed to the application processor. The application processor outputs a sound signal through an audio device (not limited to the speaker 170A, the receiver 170B, etc.), or displays an image or video through the display screen 194. In some embodiments, the modem processor may be an independent device. In other embodiments, the modem processor may be independent of the processor 110 and be provided in the same device as the radio frequency module 150 or other functional modules.

The communication module 160 can provide applications on the mobile phone 100 including wireless local area networks (WLAN) (such as wireless fidelity (Wi-Fi) networks), bluetooth (BT), and global navigation satellite systems ( Global navigation satellite system, GNSS), frequency modulation (FM), near field communication (NFC), infrared technology (infrared, IR) and other wireless communication solutions. The communication module 160 may be one or more devices integrating at least one communication processing module. The communication module 160 receives electromagnetic waves via the antenna 2, frequency modulates and filters the electromagnetic wave signals, and sends the processed signals to the processor 110. The communication module 160 may also receive the signal to be sent from the processor 110, perform frequency modulation, amplify it, and convert it into electromagnetic waves to radiate through the antenna 2.

In some embodiments, the antenna 1 of the mobile phone 100 is coupled with the radio frequency module 150, and the antenna 2 is coupled with the communication module 160, so that the mobile phone 100 can communicate with the network and other devices through wireless communication technology. The wireless communication technologies may include global system for mobile communications (GSM), general packet radio service (GPRS), code division multiple access (CDMA), broadband Code division multiple access (wideband code division multiple access, WCDMA), time-division code division multiple access (TD-SCDMA), long term evolution (LTE), BT, GNSS, WLAN, NFC , FM, and/or IR technology, etc. The GNSS may include global positioning system (GPS), global navigation satellite system (GLONASS), Beidou navigation satellite system (BDS), quasi-zenith satellite system (quasi -zenith satellite system, QZSS) and/or satellite-based augmentation systems (SBAS).

The mobile phone 100 implements a display function through a GPU, a display screen 194, and an application processor. The GPU is a microprocessor for image processing, connected to the display 194 and the application processor. The GPU is used to perform mathematical and geometric calculations for graphics rendering. The processor 110 may include one or more GPUs, which execute program instructions to generate or change display information.

The display screen 194 is used to display images, videos, etc. The display screen 194 includes a display panel. The display panel can adopt liquid crystal display (LCD), organic light-emitting diode (OLED), active-matrix organic light-emitting diode or active-matrix organic light-emitting diode (active-matrix organic light-emitting diode). AMOLED, flexible light-emitting diode (FLED), Miniled, MicroLed, Micro-oLed, quantum dot light-emitting diode (QLED), etc. In some embodiments, the mobile phone 100 may include one or N display screens 194, and N is a positive integer greater than one.

The mobile phone 100 can realize a shooting function through an ISP, a camera 193, a video codec, a GPU, a display screen 194, and an application processor.

The ISP is used to process the data fed back from the camera 193. For example, when taking a picture, the shutter is opened, the light is transmitted to the photosensitive element of the camera through the lens, the light signal is converted into an electrical signal, and the photosensitive element of the camera transfers the electrical signal to the ISP for processing and is converted into an image visible to the naked eye. ISP can also optimize the image noise, brightness, and skin color. ISP can also optimize the exposure, color temperature and other parameters of the shooting scene. In some embodiments, the ISP may be provided in the camera 193.

The camera 193 is used to capture still images or videos. In some embodiments, the mobile phone 100 may include 1 or N cameras, and N is a positive integer greater than 1. The camera 193 may be a front camera or a rear camera. As shown in FIG. 2, the camera 193 generally includes a lens and a sensor. The photosensitive element may be a CCD (charge-coupled device) or a CMOS (complementary metal oxide semiconductor, complementary metal oxide semiconductor). ) And other arbitrary photosensitive devices.

As shown in FIG. 2, during the shooting process, the reflected light of the object being photographed can generate an optical image after passing through the lens. The optical image is projected onto the photosensitive element, and the photosensitive element converts the received light signal into an electrical signal, and further, The camera 193 sends the obtained electrical signal to a DSP (Digital Signal Processing) module for digital signal processing, and finally a digital image is obtained. The digital image can be output on the mobile phone 100 through the display screen 194, or the digital image can be stored in the internal memory 121 (or the external memory 120).

Video codecs are used to compress or decompress digital video. The mobile phone 100 may support one or more video codecs. In this way, the mobile phone 100 can play or record videos in a variety of encoding formats, such as: moving picture experts group (MPEG) 1, MPEG2, MPEG3, MPEG4, etc.

NPU is a neural-network (NN) computing processor. By drawing on the structure of biological neural networks, for example, the transfer mode between human brain neurons, it can quickly process input information and can continuously learn by itself. Through the NPU, applications such as intelligent cognition of the mobile phone 100 can be realized, such as image recognition, face recognition, voice recognition, text understanding, etc.

The external memory interface 120 may be used to connect an external memory card, such as a Micro SD card, to expand the storage capacity of the mobile phone 100. The external memory card communicates with the processor 110 through the external memory interface 120 to realize the data storage function. For example, save music, video and other files in an external memory card.

The internal memory 121 may be used to store computer executable program code, where the executable program code includes instructions. The processor 110 executes various functional applications and data processing of the mobile phone 100 by running instructions stored in the internal memory 121. The internal memory 121 may include a storage program area and a storage data area. Among them, the storage program area can store an operating system, at least one application program (such as a sound playback function, an image playback function, etc.) required by at least one function. The data storage area can store data (such as audio data, phone book, etc.) created during the use of the mobile phone 100. In addition, the internal memory 121 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one magnetic disk storage device, a flash memory device, a universal flash storage (UFS), etc.

The mobile phone 100 can implement audio functions through the audio module 170, the speaker 170A, the receiver 170B, the microphone 170C, the earphone interface 170D, and the application processor. For example, music playback, recording, etc.

The audio module 170 is used to convert digital audio information into an analog audio signal for output, and is also used to convert an analog audio input into a digital audio signal. The audio module 170 can also be used to encode and decode audio signals. In some embodiments, the audio module 170 may be provided in the processor 110, or part of the functional modules of the audio module 170 may be provided in the processor 110.

The speaker 170A, also called a "speaker", is used to convert audio electrical signals into sound signals. The mobile phone 100 can listen to music through the speaker 170A, or listen to a hands-free call.

The receiver 170B, also called "earpiece", is used to convert audio electrical signals into sound signals. When the mobile phone 100 answers a call or a voice message, it can receive the voice by bringing the receiver 170B close to the human ear.

The microphone 170C, also called "microphone", "microphone", is used to convert sound signals into electrical signals. When making a call or sending a voice message, the user can approach the microphone 170C through the mouth to make a sound, and input the sound signal to the microphone 170C. The mobile phone 100 may be provided with at least one microphone 170C. In other embodiments, the mobile phone 100 may be provided with two microphones 170C, which can implement noise reduction functions in addition to collecting sound signals. In other embodiments, the mobile phone 100 may also be provided with three, four or more microphones 170C to collect sound signals, reduce noise, identify sound sources, and realize directional recording functions.

The earphone interface 170D is used to connect wired earphones. The earphone interface 170D may be a USB interface 130, or a 3.5mm open mobile terminal platform (OMTP) standard interface, or a cellular telecommunications industry association (cellular telecommunications industry association of the USA, CTIA) standard interface.

The sensor module 180 may include a pressure sensor 180A, a gyroscope sensor 180B, an air pressure sensor 180C, a magnetic sensor 180D, an acceleration sensor 180E, a distance sensor 180F, a proximity light sensor 180G, a fingerprint sensor 180H, a temperature sensor 180J, a touch sensor 180K, and an ambient light sensor 180L, bone conduction sensor 180M, etc.

The button 190 includes a power button, a volume button, and so on. The button 190 may be a mechanical button. It can also be a touch button. The mobile phone 100 can receive key input, and generate key signal input related to user settings and function control of the mobile phone 100.

The motor 191 can generate vibration prompts. The motor 191 can be used for incoming call vibration notification, and can also be used for touch vibration feedback. For example, touch operations applied to different applications (such as photographing, audio playback, etc.) can correspond to different vibration feedback effects. Acting on touch operations in different areas of the display screen 194, the motor 191 can also correspond to different vibration feedback effects. Different application scenarios (for example: time reminding, receiving information, alarm clock, games, etc.) can also correspond to different vibration feedback effects. The touch vibration feedback effect can also support customization.

The indicator 192 may be an indicator light, which may be used to indicate the charging status, power change, or to indicate messages, missed calls, notifications, and so on.

The SIM card interface 195 is used to connect to the SIM card. The SIM card can be connected to and separated from the mobile phone 100 by inserting into the SIM card interface 195 or pulling out from the SIM card interface 195. The mobile phone 100 may support 1 or N SIM card interfaces, and N is a positive integer greater than 1. The SIM card interface 195 can support Nano SIM cards, Micro SIM cards, SIM cards, etc. The same SIM card interface 195 can insert multiple cards at the same time. The types of the multiple cards can be the same or different. The SIM card interface 195 can also be compatible with different types of SIM cards. The SIM card interface 195 may also be compatible with external memory cards. The mobile phone 100 interacts with the network through the SIM card to implement functions such as call and data communication. In some embodiments, the mobile phone 100 uses an eSIM, that is, an embedded SIM card. The eSIM card can be embedded in the mobile phone 100 and cannot be separated from the mobile phone 100.

In the embodiment of the present application, the mobile phone 100 can detect the user's health data through one or more sensors in the sensor module 180, and the health data can reflect the user's life habit characteristics. For example, the health data may include the user’s daily exercise data (such as exercise time, exercise volume, etc.), sleep data (such as time to fall asleep, sleep duration, etc.), nutritional intake data (such as calories intake, meal time, etc.), and usage One or more items of data such as the time and duration of the phone. The mobile phone 100 may store the detected health data of the most recent period of time (for example, the most recent six months, the most recent month, the most recent week, the most recent day, or the most recent several hours) in the external memory 120 or the internal memory 121 of the mobile phone 100.

Alternatively, as shown in FIG. 3, the mobile phone 100 may also interact with the wearable device 200 through the communication module 160. Among them, the wearable device 200 may be a smart watch, a smart bracelet, smart glasses, a smart helmet, or a smart headset, and the embodiment of the present application does not impose any limitation on this. After the mobile phone 100 establishes a connection with the wearable device 200, the wearable device 200 can send the collected health data of the user to the mobile phone 100. Of course, the user can also manually input his own health data into the mobile phone 100, and the embodiment of the present application does not impose any restriction on this.

Exemplarily, an application with a facial appearance prediction function (may be called a prediction APP) may be installed in the mobile phone 100. If it is detected that the user has turned on the facial prediction function in the prediction APP, the mobile phone 100 can obtain data containing the user's facial image, and the mobile phone 100 can obtain the user's health data within a preset time period (recent period). Furthermore, in combination with the user's recent health data, the mobile phone 100 can predict the change in the user's appearance after a period of time (for example, 1 year, 3 years, 5 years, or 10 years) based on the user's facial image, thereby generating an image containing the result of the appearance change And display the image to the user.

In this way, the appearance change predicted by the mobile phone 100 for the user can change with the user's health data, that is, the appearance change predicted by the mobile phone 100 for the user is closely related to the user's actual living habits. For example, if the user’s actual life habits are relatively healthy, the mobile phone 100 predicts that the user’s appearance will age relatively slowly; if the user’s actual life habits are not healthy, the user’s appearance predicted by the mobile phone 100 will be relatively slow. fast. It can be seen that the mobile phone 100 not only considers the factors of the passage of time when predicting the user's appearance for the user, but also combines the user's health data, so that the prediction result of the user's appearance is more accurate.

In addition, when the mobile phone 100 displays the predicted image of the user after aging, it can also remind the user of the current unhealthy living habits, or remind the user of how these unhealthy living habits affect the appearance. In this way, the user can more intuitively and vividly learn the impact of the current living habits on the appearance, thereby reminding and urging the user to establish healthier living habits through the way of appearance prediction.

Hereinafter, a face prediction method provided by an embodiment of the present application will be described in detail with reference to the accompanying drawings. As shown in FIG. 4, taking a mobile phone as an example of an electronic device, the method may include steps S401-S404.

S401. The mobile phone acquires a first image, where the first image contains a facial image of the user (also referred to as a first facial image).

Exemplarily, an APP with a facial appearance prediction function (referred to as a prediction APP in the subsequent embodiments) may be installed in the mobile phone. The prediction APP may be a photo APP, a Meitu APP, a sports APP, or a health APP. This application The embodiment does not impose any limitation on this. As a possible implementation, the face prediction function can also be set as a function option in the negative screen of the mobile phone or the drop-down menu of the mobile phone. When the user clicks the facial prediction function on the negative screen of the mobile phone or the drop-down menu of the mobile phone, the mobile phone can display the interactive interface 501 shown in FIG. 5. Here, the user clicks the prediction APP as an example for description, but it does not constitute a limitation.

If it is detected that the user starts the prediction APP operation, as shown in FIG. 5, the mobile phone can start the prediction APP and display the interactive interface 501 of the prediction APP. The interactive interface 501 can be provided with a button for predicting facial features, such as the measuring button 502 shown in FIG. 5, and the button 502 can be used to enable the facial predicting function. If it is detected that the user clicks the button 502, the mobile phone can call the camera APP to open the camera to capture the current shooting picture.

As shown in FIG. 6, the mobile phone can display the captured image 601 in the preview interface 602. In addition, the mobile phone can prompt the user to move the mobile phone to input the face into the shooting screen 601. For example, the mobile phone may prompt the user to use the front camera to take a facial image through text in the preview interface 601. For another example, the mobile phone may prompt the user to look up at the rear camera in the form of voice, and adjust the distance between the mobile phone and the user, so that the mobile phone can capture the user's facial image in the shooting screen 601, that is, the first facial image.

The mobile phone can use the preset face detection algorithm when capturing the shooting screen 601 to identify whether the shooting screen 601 contains a face of a preset size. If it detects that the shooting screen 601 contains a face of a preset size, the phone can automatically Perform a photographing operation to obtain an image (ie, a first image) in the currently photographed screen 601, and the first image contains a facial image of the user (ie, a first facial image). Of course, the user can also manually click the camera button 603 in the preview interface 602. In response to the user's operation of clicking the camera button 603, the mobile phone can save the captured image 601 acquired at this time as the first image in the memory.

In other embodiments, the mobile phone may also prompt the user to select a photo containing the user's face from the album after detecting that the user has turned on the aforementioned facial prediction function. Furthermore, the mobile phone can extract the user's facial image (that is, the first facial image) from the photo selected by the user through a face detection algorithm. Of course, the above-mentioned first facial image is still obtained by the mobile phone from the server or other electronic devices, and the embodiment of the present application does not impose any limitation on this.

In addition to acquiring the first image containing the user's facial image, as shown in Figure 7, the mobile phone can also prompt the user to enter his current age. In this way, the mobile phone can subsequently predict the user's appearance based on the user's current age. For another example, as shown in FIG. 7, the mobile phone may also prompt the user to select how long the appearance change needs to be predicted. For example, the user may choose to predict his appearance in 1 year, 5 years, or 10 years. Of course, the mobile phone can also automatically recognize the age of the user based on the first image acquired, or the mobile phone can predict the user’s appearance after a certain period of time (for example, 5 years) based on the acquired first image. There are no restrictions.

S402. The mobile phone obtains the user's health data within a preset time.

After the mobile phone detects that the user has turned on the above facial prediction function, it can also obtain the user's health data in the most recent period of time (for example, one week, one month, three months, six months, or one year). The health data can reflect the characteristics of the user's living habits.

For example, a mobile phone can record health data such as the user's daily work and rest time, sleep quality, meal time, calorie intake, exercise time, and exercise volume. Alternatively, the mobile phone may also obtain one or more items of health data of the user detected by the wearable device from the wearable device of the user. Alternatively, the mobile phone can also record various health data manually entered by the user. Then, after detecting that the user has turned on the aforementioned facial prediction function, the mobile phone can obtain various health data recorded by the mobile phone within a preset time period. For example, the mobile phone can generate an n (n>0)-dimensional matrix according to the acquired n items of health data, and each dimensional vector in the matrix corresponds to one item of health data. As shown in (a) in Figure 8, matrix A contains the user's daily health data acquired by the mobile phone in the last month (30 days). The health data includes the user's daily sleep time, walking steps, calorie intake, and mobile phone usage time. Each row vector in matrix A corresponds to a piece of health data.

It should be noted that the embodiment of the present application does not make any limitation on the execution sequence between the foregoing steps S401 and S402. For example, the mobile phone can obtain the user's health data in the last month after obtaining the user's first image. For another example, the mobile phone can also monitor the user's health data in the last month in real time. If unhealthy behaviors are detected in the user's health data, for example, if it is detected that the user sleeps for less than 5 hours in a continuous week, the mobile phone can automatically turn on The above-mentioned facial appearance prediction function obtains the most recently taken photo containing the user's facial image from the album, and then performs the following steps S403-S404 to remind the user of the impact of unhealthy behaviors on the appearance.

S403. The mobile phone predicts the appearance of the facial image in the first image based on the aforementioned health data and a preset number of years (for example, 5 years).

Taking the mobile phone as an example of predicting the user's appearance in 5 years based on the facial image obtained in step S401, when the mobile phone predicts the user's appearance change in 5 years, in addition to considering the influence of time on the user's appearance, it is also based on the information obtained in step S402. The user's health data predicts an image of the user's face in 5 years (also called a second facial image). In other words, the mobile phone can predict for the user the impact on the user's appearance when this lifestyle continues based on the current user's living habits, thereby reminding and urging the user to establish a healthier lifestyle.

In the following, various methods for predicting the appearance of a user's facial image in combination with health data provided by the examples of this application will be described in detail.

method one

The mobile phone can extract the user's life habit characteristics from n items of health data of the user in the most recent period of time (for example, one month). Exemplarily, the mobile phone can calculate the characteristic value of each item of health data in n items of health data. For example, as shown in Figure 8(b), for the health data of sleep time, the mobile phone can calculate the user’s daily average sleep time b _{1 in the} last month; for the health data of walking steps, the mobile phone can calculate The user’s daily average number of walking steps b _{2 in the} last month; for the health data of calorie intake, the mobile phone can calculate the user’s daily average calorie intake in the last month b ₃ ; 1. Health data, the mobile phone can calculate the user's daily average mobile phone usage time b _{4 in the} last month. Of course, in addition to calculating the average value of health data over a period of time, mobile phones can also use other algorithms to extract corresponding feature values from each item of health data. In this way, as shown in Fig. 8 (b), the mobile phone can obtain an n-dimensional feature vector B, and each value in the n-dimensional feature vector B represents a feature value of a piece of health data. The entire n-dimensional feature vector can reflect the characteristics of the user's living habits. For example, if the user's sleep time is later than 12 o'clock in the n-dimensional feature vector, the user has a living habit characteristic of going to bed late.

Furthermore, the mobile phone can determine the age influence value corresponding to the user's life habit characteristics based on the user's life habit characteristics. Age influence value refers to the deviation value that is positively or negatively correlated with facial features of a certain fixed age. For example, for a 28-year-old user, if the user's living habit characteristics are relatively healthy, the user's facial image may show a 26-year-old facial feature, that is, the age influence value at this time is -2 years old. Correspondingly, for a 28-year-old user, if the user's life habit characteristics are not healthy, the user's facial image may show a 31-year-old facial feature, that is, the age influence value at this time is 3 years old. Then, in mode one, the mobile phone can determine the age influence value corresponding to the life habit characteristic of the user according to the determined life habit characteristic of the user.

Exemplarily, algorithms such as decision trees or convolutional neural networks (Convolutional Neural Networks, CNN) may be used in advance to train the correspondence between different living habits features and corresponding age influence values. For example, a mobile phone or a server can collect a large number of facial images of users of different ages and different life habits for machine learning and training, thereby establishing an input and output model of different life habits characteristics-different age influence values. In this way, after the mobile phone inputs the determined characteristics of the user's living habits (for example, the aforementioned n-dimensional feature vector) into the input-output model, the input-output model can output the corresponding age influence value. Generally, the healthier the user's living habits, the smaller the corresponding age influence value, and the less healthy the user's living habits, the larger the corresponding age influence value.

It should be noted that the above-mentioned input and output model can be set in the mobile phone or in the server. When the input and output model is set on the server, the mobile phone can send the determined characteristics of the user's life habit to the server, and the server uses the input and output model to determine the corresponding age influence value. Alternatively, the mobile phone may also send the acquired health data to the server, and the server extracts the living habits characteristics in the health data, and determines the corresponding age influence value. Alternatively, the user's health data can also be stored in the server, and the mobile phone can send a prediction instruction to the server, so that the server obtains the user's health data, and determines the age influence value corresponding to the user's lifestyle characteristics according to the above method.

Still taking the mobile phone as an example of predicting the user's appearance in 5 years, as shown in FIG. 9, if the user's current age is 27 years old, it means that the user needs to predict the facial image 801 when the user is 32 years old. Due to the influence of the user's current living habit characteristics, if the mobile phone determines the corresponding age influence value is -2 years old, it means that when the user is 32 years old, the face actually presents the facial features of 30 years old. Therefore, the mobile phone can predict the facial image 802 when the user is 30 years old based on the facial image in the first image, and display the predicted result to the user as the facial image when the user is 32 years old.

Correspondingly, if the mobile phone determines that the corresponding age influence value is 3 years old, it means that when the user is 32 years old, the face actually presents the facial features of 35 years old. Then, the mobile phone can predict the user's face image at the age of 35 based on the face image in the first image, and display the predicted result to the user as the user's face image at the age of 32.

Exemplarily, the mobile phone may input the facial image in the first image into the preset prediction model, and input the current age of the user and the target age that needs to be predicted into the prediction model. In Method 1, the target age is the result of the superposition of the age that the user actually needs to predict and the above-mentioned age influence value. For example, the actual age that the user needs to predict is 30 years old. If the mobile phone determines the corresponding age influence value according to the user's health data as 2 years old, the target age that the mobile phone needs to predict this time is 32 years old. Correspondingly, if the mobile phone determines that the corresponding age influence value is -2 years old according to the user's health data, the target age that the mobile phone needs to predict this time is 28 years old. Furthermore, the prediction model may use an aging processing algorithm to predict the user's facial image at the target age based on the user's facial image in the first image and the current age, and obtain a second image containing the user's face after aging processing.

Exemplarily, a mobile phone or server can create a sample pool, which contains a large number of facial images of users of different ages. Furthermore, the mobile phone or the server can perform in-depth learning based on facial images of users of different ages, so as to establish the aforementioned prediction model. For example, the server may train and learn facial images of users of different ages based on Generative Adversarial Nets (GAN) to establish the above prediction model.

Generally, GAN includes a generative model (generative model) and a discriminative model (discriminative model). As shown in Figure 10, the user's current real facial image (such as the first image) and target age label can be input into the generation model, and the generation model can generate a face corresponding to the target age according to the user's current real facial image and target age label. Predict the image, and the discrimination model can determine whether the facial prediction image output by the generation model is a real image or a generated image based on the current user's real facial image, target age label, and facial prediction image output by the generation model. Then, through continuous iteration and training of the entire GAN, when the generative model and the discriminant model reach a balance, the generative model can generate images that are "false and true", so that the discriminant model cannot judge the authenticity of the facial image output by the generative model. At this time, the facial image output by the generation model is the facial image that the user has aged at the target age.

It can be seen that in the first method, when the mobile phone predicts the user's facial image in M years, the user's health data can be combined with the user's health data to determine the age influence value X of the user's living habits. Furthermore, the mobile phone can actually predict the face image for the user in M+X years, so that the predicted face image can not only reflect the change of the user’s face with age, but also reflect the impact of the user’s current living habits on the user’s appearance , So as to provide the accuracy of facial prediction and user experience.

Way two

In the second method, the mobile phone can also input the facial image in the first image into the prediction model, and input the current age of the user and the target age to be predicted into the prediction model. The difference is that in the second method, the target age refers to the actual age selected by the user after M years or the user's age after a certain period of time by default on the mobile phone.

For example, if the user chooses to predict facial images after 5 years in the interface shown in Figure 7, as shown in Figure 11, the mobile phone can use the above prediction model based on the user’s facial image obtained in step S401 and the user’s current age (for example, 27 Years old), predict the facial image 901 after 5 years (that is, when the user is 32 years old).

Furthermore, the mobile phone can add a corresponding facial effect to the predicted facial image 901 based on the acquired health data of the user. For example, the appearance effect may include one or more of changes in skin luster, changes in skin color, changes in wrinkles, changes in pigmentation, or changes in face shape.

Exemplarily, a mobile phone or a server can create a sample pool, which contains a large number of facial images of users with different lifestyle characteristics. Furthermore, the mobile phone or the server can perform in-depth learning based on the facial images of users with different life habit characteristics, so as to establish the correspondence between different life habit characteristics and different facial effects. For example, when the user has the habit of going to bed late, the corresponding appearance effect of dark circles; when the user has the habit of overeating, the corresponding appearance effect of facial obesity.

Then, after the mobile phone extracts the corresponding life habit characteristics from the user's health data, it can query the appearance effect corresponding to the life habit characteristics extracted this time in the local or server. Furthermore, as shown in FIG. 11, the mobile phone can add these facial effects to the predicted facial image 901 of the user after M years, so that the mobile phone can finally obtain the user's prediction for the user after M years based on the user's current living habits. Face image 902.

That is to say, as shown in Figure 12, in the second mode, two models can be set in the mobile phone, one is the facial image prediction model 1 based on age (that is, the above prediction model), and the other is the facial image prediction based on lifestyle habits. Model 2 (ie the above-mentioned correspondence). After the mobile phone extracts the user's facial image from the first image, the mobile phone can input the facial image in the first image, the user's current age, and the target age to be predicted into the prediction model (ie, prediction model 1). In this way, the mobile phone uses the prediction model 1 to predict the facial image 901 of the user at the target age over time. Furthermore, the mobile phone can input the n-dimensional feature vector extracted by the mobile phone from n items of health data into the prediction model 2, and the n-dimensional feature vector can reflect the user's living habits. In addition, the facial image 901 output by the prediction model 1 can also be input to the prediction model 2. In this way, on the basis of the facial image 901, the mobile phone can use the prediction model 2 to predict the user's facial image 902 in M years based on the user's living habits.

It can be seen that in the second method, when the mobile phone predicts the user's facial image in M years, it can combine the user's health data to add the corresponding facial image to the facial image predicted by the mobile phone for the user in M years, so that the prediction is The facial image can not only reflect the changes of the user’s face with age, but also reflect the impact of the user’s lifestyle on the user’s appearance, thereby providing the accuracy of facial prediction and the user’s experience.

Way Three

In the third method, the mobile phone or the server can create a prediction model that uses the two parameters of age and life habit characteristics as variables to predict facial aging images corresponding to different ages and different life habit characteristics. For example, the server may establish a face photo library of different age groups and a face photo library of different living habits. Furthermore, the server can mine and learn the image features in the two face photo libraries through a deep learning algorithm, so as to establish a prediction model of the interaction between age, living habits, and facial images.

Then, after the user turns on the above facial prediction function, the mobile phone can extract the user's facial image from the first image obtained in step S401, and the mobile phone can extract the user's life habit characteristics from the health data obtained in step S402. Furthermore, the mobile phone can input the user's facial image, life habit characteristics, and the target age that needs to be predicted into the prediction model to obtain the user's facial image predicted for the user in M years based on the user's current life habits.

It should be noted that the above-mentioned method 1 to method 3 only illustrate how to predict the facial image of the user in M years for the user based on the user's living habits. It is understandable that a person skilled in the art can set a specific algorithm, model, or implementation method for predicting a user's facial image in M years based on the user's living habits according to actual application scenarios or actual experience, and the embodiment of the present application does not impose any limitation on this.

S404. The mobile phone displays a second image, and the second image contains the facial image of the user obtained after facial appearance prediction.

After the above step S403, after the mobile phone predicts the facial image of the user in the first image, a second image can be obtained. The second image includes the facial image predicted for the user after a period of time (ie, the second facial image). The facial image in the second image is associated with the user's health data. Then, in step S404, as shown in (a) of FIG. 13A, the mobile phone can display the second image 1002 in the interface 1001 of the prediction APP, so that the user can intuitively and vividly see if the current lifestyle habits are maintained What impact will it have on the appearance of users after M years?

Exemplarily, a button 1003 corresponding to the current time and a button 1004 corresponding to the M years (for example, 5 years) in which the prediction is needed may also be set in the interface 1001 of the prediction APP. If it is detected that the user clicks on the button 1003, as shown in (b) in FIG. 13A, the mobile phone can display the first image 1006 containing the user's facial image obtained when predicting the user's appearance this time. If it is detected that the user clicks the button 1004, as shown in (a) of FIG. 13A, the mobile phone can display the second image 1002 of the user's face predicted this time in combination with the user's health data in 5 years.

Exemplarily, as shown in (a) of Figure 13A, the mobile phone can also use text 1005 in the interface 1001 of the prediction APP to prompt the user of the specific impact of the current living habits on the user’s appearance, thereby reminding the user to adjust badly in time living habit.

Or, as shown in Figure 13B, the mobile phone can also display the current user's facial image (ie the first image 1006) and the user's facial image (ie the second image) predicted by the mobile phone 5 years later in the interface 1001 of the prediction APP. 1002). In this way, the user can intuitively compare the changes between the current appearance and the appearance after 5 years, thereby understanding the specific impact of the current bad living habits on the appearance.

In some embodiments, as shown in (a) of FIG. 14, when the mobile phone displays the second image 1002, the specific problem caused by the user's bad living habits may be marked on the specific location of the user's face. For example, if the user’s health data indicates that the user has a living habit of going to bed late, and this lifestyle will increase facial wrinkles, then the mobile phone can add a mark 1101 to the wrinkle area of the user’s face when displaying the second image 1002 to remind the user Sleeping late can exacerbate facial wrinkles. In this way, the user can intuitively understand how the current bad life habits will specifically affect the appearance, so as to remind the user to adjust the bad life habits in time.

Further, if it is detected that the user clicks on the above-mentioned mark 1101, as shown in (b) in Figure 14, the mobile phone can prompt the user to adjust the specific method or suggestion of related bad living habits through text, voice, etc., so as to help and guide the user as soon as possible Adjust bad habits.

In some embodiments, the mobile phone can also show the user how the user's facial appearance changes after different times. For example, as shown in (a) or (b) of FIG. 15, the mobile phone can set an aging progress bar 1201 and a slider 1202 in the interface 1001 of the prediction APP. The user can drag the slider 1202 in the interface 1001 to slide on the aging progress bar 1201. For example, as shown in Figure 15 (a), if it is detected that the user drags the slider 1202 to the middle point A of the aging progress bar 1201, the mobile phone can display the user’s predicted 5 years later in the interface 1001 Facial image. As shown in (b) of FIG. 15, if it is detected that the user drags the slider 1202 to the end point B of the aging progress bar 1201, the mobile phone can display the facial image predicted by the user in 10 years on the interface 1001.

Among them, the specific method for the mobile phone to predict the user's facial image after M years based on the user's health data can be referred to the related description of step S403, so it will not be repeated here. Or, after the mobile phone predicts the user's facial image A after M years, if the mobile phone needs to predict the user's facial image B after M+T years, the mobile phone can multiply the pixel value of each pixel unit in the facial image A by a corresponding The scale factor w is used to obtain the facial image B of the user after M+T years.

In other words, when the user drags the slider 1201 to slide on the aging progress bar 1201, the mobile phone can display facial images predicted for the user based on the current user’s living habits at different times in chronological order, so that the user can dynamically Feel the changes in facial appearance over time while maintaining current lifestyle habits.

In some other embodiments, the mobile phone can also display the facial image predicted by the mobile phone for the user M years later after the user adjusts the bad lifestyle habits to healthy lifestyle habits. Exemplarily, as shown in (a) of FIG. 16, the mobile phone may also set a healthy life button 1301 in the interface 1001 of the prediction APP. In this way, when the mobile phone displays the second image 1002 of the user 5 years later based on the user’s current living habits, if it detects that the user clicks the button 1301, the mobile phone can predict the user 5 based on the first image and preset standard health data. Face image 1302 after the year. The preset standard health data may be health data of users with relatively healthy living habits collected by mobile phones or servers.

In this way, in response to the user's operation of clicking the button 1301, as shown in (b) of FIG. 16, the mobile phone can display the facial image 1302 predicted for the user in 5 years based on the standard health data in the interface 1001 of the prediction APP. It can be seen that by comparing the two images, the user can intuitively know the impact of the current lifestyle and healthy lifestyle on the future appearance, thereby reminding and urging the user to establish a healthier lifestyle.

In some other embodiments, the mobile phone can also show the user the effect of maintaining a healthy lifestyle for different lengths of time on the facial appearance of the user. For example, as shown in (a)-(b) in FIG. 17, the mobile phone is provided with a first progress bar 1401 and a second progress bar 1402 in the interface 1001 of the prediction APP. The first progress bar 1401 is used to indicate the aging progress when the user maintains the current life habit, and the second progress bar 1402 is used to indicate the aging progress when the user maintains the standard healthy life habit. The user can drag the slider 1403 to slide on the first progress bar 1401 and the second progress bar 1402.

For example, as shown in Figure 17(a), when the user drags the slider 1403 to slide on the first progress bar 1401, the mobile phone can display the predictions for the user at different times based on the current user’s living habits. The facial image of the user can dynamically feel the changes of facial appearance over time while maintaining the current living habits. As shown in Figure 17(b), when the user drags the slider 1403 to slide on the second progress bar 1402, the mobile phone can display the predicted faces for the user at different times in chronological order based on standard healthy living habits The image allows users to dynamically feel the changes in facial appearance over time when improving current bad habits.

In other embodiments, the mobile phone may also save the facial images predicted for the user one or more times recently, and the corresponding living habits characteristics. For example, on October 1, 2018, the mobile phone predicted the facial image A for the user 3 months later based on the user's living habits at that time. The mobile phone can save the facial image A and the corresponding lifestyle feature 1. If it is detected around January 1, 2019 that the user has turned on the facial prediction function again, the mobile phone can obtain the current user's facial image B and the user's current lifestyle characteristics2. Furthermore, by comparing the facial image A and the facial image B, and comparing the life habit feature 1 and the life habit feature 2, the mobile phone can analyze the specific impact of the change in the user's life habit on the user's facial appearance.

Then, as shown in (a) of Figure 18, when the mobile phone displays the facial image B acquired this time in the interface 1501 of the prediction APP, it can also prompt the user of the change in living habits and the change in living habits in the recent period. Changes to appearance. In addition, if it is detected that the user clicks on the historical prediction record button 1502 in the interface 1501, as shown in (b) in Figure 18, the mobile phone can display facial images of the same period that have been predicted for the user based on the user's historical living habits ( For example, the aforementioned facial image A) allows users to intuitively see the impact of changes in their own habits on their appearance.

In other embodiments, in addition to predicting changes in the user's facial appearance based on the user's health data, the mobile phone can also predict changes in the user's body shape, such as changes in weight, obesity, hunchback, and O-legs. For example, a mobile phone or a server can use GAN to train the correspondence between different lifestyle characteristics and different body shape data. Then, when the mobile phone displays the user's facial image before and after the prediction, it can also load the user's facial image on the corresponding figure template and display it to the user.

Take the example of using GAN to predict the change of the user's body shape based on the user's weight. As shown in Figure 19, the real image containing the user's real body shape and the target weight label can be input into the GAN generation model. The generated model can be based on the user's current real body shape and target weight The tag generates a body shape prediction image corresponding to the target weight, and the discriminant model can determine whether the body shape prediction image output by the generation model is a real image or a generated image based on the user's current real body shape, target weight label, and the body shape prediction image output by the generation model . Then, through continuous iteration and training of the entire GAN, when the generative model and the discriminant model reach a balance, the generative model can generate images that are "false and true", so that the discriminant model cannot judge the authenticity of the facial image output by the generative model. At this time, the facial image output by the generating model is the body shape template image corresponding to the user's weight as the target weight.

Exemplarily, as shown in Figure 20(a), when the mobile phone displays the facial image 1601 acquired this time in the interface 1501 of the prediction APP, the facial image 1601 can be loaded on the preset body shape template 1602 for display . If it is detected that the user clicks the button 1600 in 5 years, the mobile phone can predict the user's facial image 1603 based on the user's health data, and the mobile phone can also predict the user's body shape data in 5 years based on the user's health data . Furthermore, as shown in (b) of FIG. 20, the mobile phone may load the predicted facial image 1603 on the body shape template 1604 corresponding to the predicted body shape data for display. In this way, the user can intuitively and vividly understand the influence of his own life habits on his body shape in the future, thereby reminding the user to adjust bad life habits in time.

In some other embodiments of the present application, the mobile phone can also actively push the predicted facial image to the user based on the acquired health data. For example, as shown in Figure 21, after the mobile phone obtains the user's health data within the most recent preset time (for example, the most recent month), the mobile phone can determine whether the health data meets the preset conditions. The preset condition may be that the health data is greater than a certain preset value or less than a certain preset value. For example, if it is detected in the user's health data that the user's sleep time for a continuous week is less than 5 hours, the mobile phone can determine that the health data does not meet the preset conditions. At this time, the mobile phone can automatically turn on the aforementioned facial prediction function to obtain the user's real facial image (i.e., the first facial image). Furthermore, by performing the above steps S403-S404, the mobile phone can predict the user's facial image (i.e., the first facial image) in M years. Two facial images). Since the user's health data does not meet the preset conditions, indicating that the user's current living habits are unhealthy, the second facial image predicted by the mobile phone for the user based on the health data is the result of the user's facial aging.

Correspondingly, if the mobile phone can determine that the user’s health data satisfies the above preset conditions, the mobile phone can also automatically turn on the above facial prediction function to obtain the user’s real facial image (ie, the first facial image), and predict by performing the above steps S403-S404 The facial image of the user M years later (that is, the third facial image) is displayed. The difference is that since the user's health data meets the above preset conditions, it indicates that the user's current living habits are relatively healthy. Therefore, the third facial image predicted by the mobile phone for the user based on the health data is the result of the user's face de-aging.

As still shown in FIG. 21, after the mobile phone predicts the second facial image or the third facial image of the user in M years based on the user's health data, the mobile phone can actively push a notification message that the facial prediction is completed to the user. Furthermore, if it is detected that the user has opened the notification message, the mobile phone can display the second facial image or the third facial image predicted by the user in M years in the interface of the prediction APP, thereby reminding the user of the current lifestyle habits and appearance. The specific impact caused. Of course, when displaying the predicted second facial image or the third facial image, the mobile phone may also display the above-mentioned first facial image to show the user the comparison effect before and after the facial prediction. For another example, when displaying the predicted second facial image or the third facial image, the mobile phone can also display the body shape template corresponding to the second facial image or the third facial image, thereby prompting the user of the specific influence of the current living habits on the body shape The embodiment of this application does not impose any restriction on this.

It should be noted that in addition to predicting the user’s facial appearance and body shape, the mobile phone can also predict the user’s health risks, potential diseased parts and other health problems based on the user’s health data, reminding the user to pay attention to current bad habits and promptly For improvement, the embodiment of this application does not impose any restriction on this.

As shown in FIG. 22, an embodiment of the present application discloses an electronic device, including: a touch screen 2201, the touch screen 2201 includes a touch-sensitive surface 2206 and a display screen 2207; one or more processors 2202; a memory 2203; one or more One application program (not shown); and one or more computer programs 2204. The above-mentioned devices can be connected through one or more communication buses 2205. Wherein, the one or more computer programs 2204 are stored in the aforementioned memory 2203 and configured to be executed by the one or more processors 2202, and the one or more computer programs 2204 include instructions, and the aforementioned instructions can be used to execute the aforementioned Each step in the embodiment should be implemented.

Exemplarily, the foregoing processor 2202 may specifically be the processor 110 shown in FIG. 1, the foregoing memory 2203 may specifically be the internal memory 121 and/or the external memory 120 shown in FIG. 1, and the foregoing display screen 2207 may specifically be FIG. As shown in the display screen 194, the aforementioned sensor 2208 may specifically be one or more sensors in the sensor module 180 shown in FIG. 1, and the aforementioned touch-sensitive surface 2206 may specifically be the touch sensor 180K in the sensor module 180 shown in FIG. The embodiment of this application does not impose any restriction on this.

In some embodiments, this application also provides a graphical user interface (GUI), which can be stored in an electronic device. Exemplarily, the electronic device may be the electronic device shown in FIG. 1 or FIG. 22.

In some embodiments, the above-mentioned graphical user interface includes: a first GUI displayed on the touch screen, and the first GUI includes buttons for a facial prediction function; for example, the first GUI may be the interface 501 of the prediction application shown in FIG. 5, The interface 501 includes a button 502 for the facial prediction function. If it is detected that the user has performed a touch event on the button, the electronic device may obtain the user's health data and the current user's real first facial image, such as the image 601 shown in FIG. 6. Furthermore, the electronic device may predict the second facial image of the user in M years based on the first facial image and health data. Then, the foregoing graphical user interface may also include a second GUI displayed on the touch screen. For example, the second GUI may be the interface 1001 shown in FIG. 13B. The second GUI includes the user's first facial image 1006 and second facial image. Image 1002, or, the second GUI may be the interface 1001 shown in (a) of FIG. 13A, and the second GUI includes the user's second facial image 1002.

In other embodiments, the above-mentioned graphical user interface includes: a first GUI displayed on the touch screen, and the first GUI includes a notification message that the appearance prediction is completed; for example, after the electronic device obtains the user's health data, if it detects health If the data does not meet the preset condition, the electronic device can predict the user's aging second facial image in M years based on the user's first facial image and health data; or, if the health data is detected to meet the preset condition, the electronic device The device can predict the de-aging second facial image of the user in M years based on the user's first facial image and health data. Furthermore, the electronic device may display the aforementioned notification message; if it is detected that the user performs a touch event on the notification message, the electronic device may display a second GUI on the touch screen, the second GUI including a second facial image predicted for the user. Of course, the second GUI may also include the user's first facial image, etc., which is not limited in the embodiment of the present application.

Through the description of the above embodiments, those skilled in the art can clearly understand that for the convenience and brevity of the description, only the division of the above-mentioned functional modules is used as an example for illustration. In practical applications, the above-mentioned functions can be allocated as required. It is completed by different functional modules, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. For the specific working process of the system, device, and unit described above, reference may be made to the corresponding process in the foregoing method embodiment, which is not repeated here.

The functional units in the various embodiments of the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units may be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or software functional unit.

If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the embodiments of the present application essentially or the part that contributes to the prior art or all or part of the technical solutions can be embodied in the form of software products, and the computer software products are stored in a storage The medium includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) or a processor to execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage media include: flash memory, mobile hard disk, read-only memory, random access memory, magnetic disk or optical disk and other media that can store program codes.

The above are only specific implementations of the embodiments of the present application, but the protection scope of the embodiments of the present application is not limited to this. Any changes or substitutions within the technical scope disclosed in the embodiments of the present application shall be covered by this Within the protection scope of the application embodiments. Therefore, the protection scope of the embodiments of the present application should be subject to the protection scope of the claims.

Claims

A face prediction method, characterized in that it includes:

The electronic device acquires a first image, where the first image includes a first facial image of the user;

The electronic device acquires health data of the user within a preset time, and the health data includes at least one of exercise data, sleep data, nutritional intake data, or duration data of the electronic device of the user ；

The electronic device predicts the appearance of the user based on the health data and the first facial image to obtain a second image, and the second image includes the second facial image of the user;

The electronic device displays a first interface, the first interface includes the second facial image, or, the first interface includes the first facial image and the second facial image, and the first interface includes the first facial image and the second facial image. A facial image is different from the second facial image.
The method according to claim 1, wherein before the electronic device acquires the first image, further comprising:

The electronic device displays a second interface of the prediction application, and the second interface includes a button for a facial prediction function;

Wherein, acquiring the first image by the electronic device includes:

In response to the user's operation of clicking the button, the electronic device uses a camera to obtain the first image; or,

In response to the user's operation of clicking the button, the electronic device obtains a photo from the photo album application as the first image.
The method according to claim 1, wherein before the electronic device acquires the first image, further comprising:

The electronic device determines that the health data satisfies a preset condition, and the preset condition includes that the health data is greater than a preset value, or the health data is less than a preset value.
The method according to claim 3, wherein after the electronic device predicts the user's appearance based on the health data and the first facial image to obtain the second image, the method further comprises:

The electronic device displays a notification message that the appearance prediction is completed;

Wherein, the electronic device displaying the first interface includes:

In response to the user's operation of opening the notification message, the electronic device opens the prediction application and displays the first interface of the prediction application.
The method according to any one of claims 1-4, wherein the electronic device predicts the user's appearance based on the health data and the first facial image to obtain a second image, comprising :

The electronic device determines the corresponding age influence value K according to the health data, and the age influence value refers to a deviation value that is positively or negatively correlated with the user's current age;

The electronic device predicts the second facial image of the user in M+K years based on the first facial image to obtain the second image including the second facial image, where M is the default value Or the value set by the user.
The method according to any one of claims 1-4, wherein the electronic device predicts the user's appearance based on the health data and the first facial image to obtain a second image, comprising :

The electronic device predicts a third facial image of the user after M years based on the first facial image, where M is a default value or a value set by the user;

The electronic device adds a corresponding facial effect to the third facial image according to the health data to obtain the second facial image of the user after M years, and the facial effect includes a youthful effect or an aging effect.
The method according to any one of claims 1-6, wherein the first interface includes the second facial image and a first switching button;

Wherein, after the electronic device displays the first interface, it further includes:

In response to the user's operation of clicking the first switching button, the electronic device switches the displayed second facial image to the first facial image.
The method according to any one of claims 1-6, wherein the first interface includes the second facial image and a second switch button;

Wherein, after the electronic device displays the first interface, it further includes:

In response to the user's operation of clicking the second switching button, the electronic device switches the displayed second image to a standard facial image, the standard facial image corresponding to preset standard health data.
The method according to any one of claims 1-8, wherein the electronic device displaying the first interface comprises:

The electronic device marks the appearance change corresponding to the health data in the second facial image of the first interface.
The method according to any one of claims 1-9, wherein the first interface further includes methods or suggestions for adjusting the user's living habits.
The method according to any one of claims 1-10, wherein the first interface further includes an aging progress bar and a slider; the second facial image includes a first predicted image and a second facial image of the user's face. Two prediction images;

Wherein, the electronic device displaying the second facial image on the first interface includes:

If it is detected that the slider is dragged to the first position of the aging progress bar, the electronic device displays a first predicted image corresponding to the first position, and the first predicted image is the predicted The facial image of the user after the first time period;

If it is detected that the slider is dragged to the second position of the aging progress bar, the electronic device displays a second predicted image corresponding to the second position, and the second predicted image is the predicted The facial image of the user after the second time period.
The method according to any one of claims 1-11, wherein the second image further includes a figure template of the user predicted for the user after a period of time, and the figure template is related to Corresponding to the health data, the body shape template includes a fattening template or a thinning template.
The method according to any one of claims 1-12, wherein the electronic device acquiring the user's health data within a preset time comprises:

The electronic device obtains the user's health data within a preset time from the wearable device.
A face prediction method, characterized in that it includes:

The electronic device obtains the user's health data within a preset time, where the health data includes at least one of the user's exercise data, sleep data, nutritional intake data, or time length data of using the electronic device;

The electronic device acquires a first image, the first image includes a first facial image of the user, the first image is acquired by the electronic device using a camera, or the electronic device is from an album application A photo obtained;

If the health data does not meet the preset condition, the electronic device displays the first facial image and the second facial image predicted for the user after a period of time in an interface, and the second facial image The image is a predicted image after the first facial image is aged;

If the health data meets the preset condition, the electronic device displays the first facial image and a third facial image predicted for the user after a period of time in an interface, and the third facial image The facial image is a predicted image after the first facial image is de-aging.
The method of claim 14, wherein:

If the health data does not meet the preset condition, the interface further includes a first figure template that carries the first facial image and a second figure template that carries the second facial image. The second body shape template is the result of the first body shape template becoming fat;

If the health data meets the preset condition, the interface further includes a first figure template that carries the first facial image and a third figure template that carries the third facial image. The trisomy template is the result of the thinning of the first body template.
The method according to claim 14 or 15, wherein the period of time is M years, and M is a default value or a value set by a user;

If the health data does not meet the preset condition, the method further includes:

The electronic device performs aging processing on the first facial image based on the health data and the M years to obtain the second facial image of the user after M years;

If the health data meets the preset condition, the method further includes:

The electronic device performs de-aging processing on the first facial image based on the health data and the M years to obtain the third facial image of the user after M years.
An electronic device, characterized in that it comprises:

A touch screen, the touch screen including a touch-sensitive surface and a display screen;

One or more processors;

One or more memories;

One or more sensors;

And one or more computer programs, wherein the one or more computer programs are stored in the one or more memories, and the one or more computer programs include instructions, when the instructions are executed by the electronic device When the time, the electronic device is caused to execute the appearance prediction method according to any one of claims 1-16.
A computer-readable storage medium having instructions stored in the computer-readable storage medium, characterized in that, when the instructions run on an electronic device, the electronic device is caused to execute any one of claims 1-16 The appearance prediction method described in item.
A computer program product containing instructions, characterized in that, when the computer program product runs on an electronic device, the electronic device is caused to execute the appearance prediction method according to any one of claims 1-16.
A graphical user interface GUI, the graphical user interface is stored in an electronic device, the electronic device includes a touch screen, a memory, and a processor, and the processor is used to execute one or more computer programs stored in the memory, It is characterized in that the graphical user interface includes:

A first GUI displayed on the touch screen, the first GUI including a button for a facial prediction function;

In response to the touch event for the button, a second GUI is displayed on the touch screen, the second GUI includes the user's first facial image and the second facial image, or the second GUI includes the user ’S second facial image;

Wherein, the first facial image is a real facial image of the user, and the second facial image is predicted by the electronic device for the user based on the health data of the user and the first facial image Face image after some time.
A graphical user interface GUI, the graphical user interface is stored in an electronic device, the electronic device includes a touch screen, a memory, and a processor, and the processor is used to execute one or more computer programs stored in the memory, It is characterized in that the graphical user interface includes:

A first GUI displayed on the touch screen, the first GUI including a notification message that the appearance prediction is completed;

In response to the touch event for the notification message, a second GUI is displayed on the touch screen, the second GUI includes the first facial image and the second facial image of the user, or the second GUI includes The second facial image of the user;

Wherein, the first facial image is a real facial image of the user, and the second facial image is predicted by the electronic device for the user based on the health data of the user and the first facial image Face image after some time.