TWI786291B - Face recognition method, terminal device, and computer-readable storage medium - Google Patents

Abstract

Embodiments of the present invention provide a face recognition method and a terminal device. The face recognition method includes: acquiring a face image to be recognized; performing a designated detection operation on the face image, and the designated detection operation includes glasses detection , at least one of occlusion detection and face quality evaluation detection; when the detection result of the specified detection operation is abnormal, perform a reminder operation matching the detection result.

Description

Face recognition method, terminal device, and computer-readable storage medium

The embodiments of this specification relate to the technical field of face recognition, and in particular to a face recognition method and a terminal device.

With the rapid development of various payment technologies, in order to greatly simplify the payment process, face payment came into being. Face payment is a new electronic payment method, which consists of two parts: face recognition to log in to the user's wallet account and deduct money from the wallet to complete the payment process. Among them, the process of face recognition to log in to the user's wallet account is to scan and/or take a picture of the user's face, and compare the face picture with the bottom picture in the user's wallet account to complete the identification and verification of the user's identity , so as to complete the process of debiting money from the wallet to complete the payment. However, in the current face payment method, in the process of scanning and/or taking pictures of the user's face, there will be many factors that affect the user's face picture, thereby affecting the user's identity authentication and payment success rate.

The embodiment of this specification provides a face recognition method and terminal equipment, which are used to identify factors that affect face images, ensuring that facial images are used for The success rate of user identity authentication and face payment.

The embodiment of this description adopts the following technical solutions:

The first aspect provides a face recognition method, including: acquiring a face image to be recognized; performing a specified detection operation on the face image, and the specified detection operation includes glasses detection, occlusion detection, and face detection. At least one of the quality evaluation detection; when the detection result of the designated detection operation is abnormal, a reminder operation matching the detection result is executed.

The second aspect provides a terminal device, including: an acquisition module, used to acquire a face image to be recognized; a first execution module, used to perform a specified detection operation on the face image, the The specified detection operation includes at least one of glasses detection, occlusion detection, and face quality evaluation detection; the second execution module is used to execute a reminder operation that matches the detection result when the detection result of the specified detection operation is abnormal .

A third aspect provides a terminal device, including: a memory, a processor, and a computer program stored in the memory and operable on the processor, when the computer program is executed by the processor The following steps are realized: acquiring a human face image to be identified; performing a specified detection operation on the human face image, and the specified detection operation includes at least one of glasses detection, occlusion detection and facial quality evaluation detection; When the detection result of the designated detection operation is abnormal, a reminder operation matching the detection result is executed.

In the fourth aspect, a computer-readable storage medium is provided. A computer program is stored on the computer-readable storage medium. When the computer program is executed by a processor, the following steps are implemented: acquiring a face image to be recognized; The face image performs a specified detection operation, and the specified detection operation includes at least one of glasses detection, occlusion detection, and face quality evaluation detection; The alert action for the result match.

The above at least one technical solution adopted in the embodiment of the present invention can achieve the following beneficial effects:

In the embodiment of the present invention, a specified detection operation is performed on the face image, and the specified detection operation includes at least one of glasses detection, occlusion detection, and face quality evaluation detection. When the detection result of the specified detection operation is abnormal, the factors affecting the face image can be identified, and a reminder operation matching the detection result is executed, so that the user can adjust according to the reminder to eliminate the factors affecting the face image, ensuring The subsequent use of face images for user identity authentication and the success rate of face payment.

In addition, when the detection result of the specified detection operation is abnormal, the reminder operation matching the detection result can be executed, which can accurately guide the user to reduce or even remove the influencing factors affecting the face image, so as to ensure that subsequent users can successfully complete the entire face payment process. Improve the pass rate of the whole link. It is also helpful It is a process for users to learn to use face payment, so that after users feel the wisdom of face payment, it will also benefit the popularization of face payment because of its unique user experience.

1: terminal equipment

2: Identify the terminal device

500: terminal equipment

510: Obtain the module

520: The first execution module

530: The second execution module

540: The third execution module

550: send module

The accompanying drawings described here are used to provide a further understanding of the present invention, and constitute a part of the present invention. The schematic embodiments of the present invention and their descriptions are used to explain the present invention, and do not constitute improper limitations to the present invention. In the drawings: Figure 1 is a flow chart of the face recognition method provided by an embodiment of this specification; Figure 2 is a schematic diagram of the actual application scenario of the face recognition method provided by one embodiment of this specification; Figure 3 is a schematic diagram of this specification A schematic diagram of the implementation process of the actual application scenario of the face recognition method provided by an embodiment of the specification; FIG. 4 is a block diagram of the implementation system of the actual application scenario of the face recognition method provided by an embodiment of the specification; FIG. 5 is an implementation of the specification One of the structural block diagrams of the terminal device provided in the example; FIG. 6 is the second structural block diagram of the terminal device provided in an embodiment of this specification.

In order to make the purpose of the present invention, technical solutions and advantages clearer, the following The technical solutions of the present invention will be clearly and completely described in conjunction with specific embodiments of the present invention and corresponding drawings. Apparently, the described embodiments are only some of the embodiments of the present invention, but not all of them. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

Embodiments of the present invention provide a face recognition method and terminal equipment, which are used to identify factors affecting face images, and ensure the success rate of user identity authentication and face payment using face images. An embodiment of the present invention provides a face recognition method. The subject of execution of the method may be, but not limited to, a terminal device or an apparatus or system that can be configured to execute the method provided by the embodiment of the present invention.

For ease of description, the implementation of the method will be introduced below by taking the subject of execution of the method as a terminal device capable of executing the method as an example. It can be understood that the execution subject of the method is the terminal device, which is only an exemplary description, and should not be understood as a limitation of the method.

Fig. 1 is a flowchart of a face recognition method provided by an embodiment of the present invention. The method in Fig. 1 may be executed by a terminal device, as shown in Fig. 1 , the method may include:

Step 110, acquiring a face image to be recognized.

The implementation manner of obtaining the face image to be recognized may be to obtain the face image to be recognized by scanning, or to obtain the face image to be recognized by shooting. The embodiment of the present invention does not specifically limit it.

Step 120, performing a specified detection operation on the face image.

Wherein, the specified detection operation includes glasses detection, occlusion detection and At least one of face quality assessment tests.

The glasses detection can be understood as glasses reflection detection and/or large-frame glasses detection.

Of course, the glasses detection may also be the detection of any glasses in the prior art that can affect image acquisition, which is not specifically limited in this embodiment of the present invention.

The occlusion detection can be understood as the detection of face occlusion.

The face quality evaluation detection can be understood as the detection of blurring, light intensity, etc. of the face.

Step 130, when the detection result of the specified detection operation is abnormal, perform a reminder operation matching the detection result.

The detection result needs to be determined according to the specified detection operation.

For example, if the specified detection operation is glasses detection, the detection result can be the reflection detection result; if the specified detection operation is occlusion detection, the detection result can be the occlusion detection result; if the specified detection operation is face quality evaluation detection, the detection result The detection results may be evaluated for face quality.

If the detection result of the specified detection operation is abnormal, it can be understood that if the detection result is a numerical value and the numerical value is greater than the threshold, it is determined that the detection result is abnormal; otherwise, it is determined that the detection result is normal.

Exemplarily, following the above example, if the detection result is a reflection detection result, the reflection detection result is a reflection probability, and the reflection probability is greater than a threshold, it is determined that the reflection detection result is abnormal; if the detection result is an occlusion detection result, the occlusion detection result is the occlusion probability, and the occlusion probability is greater than the threshold, then it is determined that the occlusion detection result is abnormal; if the detection result is the face quality evaluation detection result, the face quality evaluation detection result is the quality problem probability, And if the probability of the quality problem is greater than the threshold, it is determined that the detection result of the face quality assessment is abnormal. Wherein, the threshold may be determined according to the actual situation of the actual application scenario, which is not limited in this embodiment of the present invention.

The reminding operation needs to be determined according to the detection result.

Using the above example, if the detection result is a reflective detection result, the reminder operation can be an operation to remind the user to take off the glasses; if the detection result is an occlusion detection result, the reminder operation can be an operation to remind the user to remove the occlusion; if the detection result is a face If the result of the internal quality assessment is determined, the reminding operation may be an operation of reminding the user to adjust the image acquisition angle.

For example, if the face quality evaluation detection result is out of focus and blurred, the user is reminded to focus when taking pictures; if the face quality evaluation detection result is motion blur, the user is reminded not to shake when taking pictures; if the face quality evaluation detection If the result is insufficient light, the user is reminded to turn on the light or choose a location with good light to take a photo.

In the embodiment of the present invention, a specified detection operation is performed on the face image, and the specified detection operation includes at least one of glasses detection, occlusion detection, and face quality evaluation detection. When the detection result of the specified detection operation is abnormal, the factors affecting the face image can be identified, and a reminder operation matching the detection result is executed, so that the user can adjust according to the reminder to eliminate the factors affecting the face image, ensuring The subsequent use of face images for user identity authentication and the success rate of face payment.

In addition, when the detection result of the specified detection operation is abnormal, the reminder operation matching the detection result can be executed, which can accurately guide the user to reduce or even remove the influencing factors affecting the face image, so as to ensure that subsequent users can successfully complete the The entire face payment process improves the pass rate of the entire link. At the same time, it is also a process to help users learn to use face payment, so that after users feel the wisdom of face payment, it will also benefit the popularization of face payment because of its unique user experience.

Optionally, as an embodiment, if the specified detection operation is eyeglass detection, step 120 may specifically be implemented as: using the face image as the input of the reflection detection model to obtain the output reflection detection result; wherein , the reflection detection model is trained based on a predetermined number of reflection face image samples and/or non-reflection face image samples.

Wherein, the face image samples with reflections may include at least one of face image samples with reflections from glasses and face image samples with glasses with black borders; the face image samples without reflections may include At least one of face image samples wearing ordinary glasses and face image samples without glasses.

Assume that the face image samples with reflections include the face image samples with glasses reflection and the face image samples with black frame glasses, and the face image samples without reflection include the face image samples with ordinary glasses and face image samples without glasses.

In this step, the reflective detection model can be obtained as follows: First, the training data roughly includes 4 types of face image samples, which are respectively face image samples with reflective glasses, face image samples with black frame glasses, and face image samples with matching glasses. Face image samples wearing ordinary glasses and face image samples without glasses, in the same class Next, one thousand images are selected respectively; then, the reflection detection model is obtained by training one thousand face image samples of each of the four categories. Wherein, how to obtain the reflective detection model through the training of 1,000 face image samples of each of the 4 categories belongs to the prior art, and will not be repeated in the embodiments of the present invention.

In the embodiment of the present invention, the reflective detection model is obtained by training a predetermined number of reflective face image samples and/or non-reflective face image samples, and then the human face image is used as the input of the reflective detection model to obtain an output According to the reflective detection results, determine whether there are reflective factors in the face image according to the reflective detection results, effectively avoiding the influence of reflective factors on the collected face images, thereby ensuring that the subsequent use of the face image for user authentication And the success rate of face payment.

Optionally, as an embodiment, the specified detection operation is occlusion detection, and step 120 may specifically be implemented as: using the face image as an input of the occlusion detection model to obtain an output occlusion detection result; wherein, The occlusion detection model is trained based on a predetermined number of occluded human face image samples and/or non-occluded human face image samples.

Wherein, the face image samples with occlusion may include face image samples with hands occluding faces, face image samples with bangs occluding faces, face image samples with hats occluding faces, and masks occluding faces. At least one of human face image samples of a human face.

Assume that the face image samples with occlusion include face image samples with hands covering faces, face image samples with bangs covering faces, and hats covering faces. Face image samples and face image samples with masks blocking faces.

In this step, the occlusion detection model can be obtained as follows: First, the training data includes about 5 types of face image samples, which are face image samples with hands occluding faces, and face images with bangs occluding faces Samples, face image samples with hats blocking faces, face image samples with masks blocking faces, and face image samples without occlusion, respectively select a thousand images under the same category; then, through 5 One thousand face image samples of each category are trained to obtain an occlusion detection model. Wherein, how to obtain an occlusion detection model through training of 1,000 face image samples of each of the 5 categories belongs to the prior art, and will not be described in detail in the embodiments of the present invention.

In the embodiment of the present invention, the occlusion detection model is obtained by training a predetermined number of face image samples with occlusion and/or face image samples without occlusion, and then the face image is used as the input of the occlusion detection model to obtain an output Based on the occlusion detection results, it is determined whether there are occlusion factors in the face image according to the occlusion detection results, which effectively avoids the influence of occlusion factors on the collected face images, thereby ensuring that the subsequent use of the face image for user identity authentication And the success rate of face payment.

Optionally, as an embodiment, if the designated detection operation is face quality evaluation detection, step 120 can be specifically implemented as: using the face image as the input of the face quality evaluation detection model to obtain an output The facial quality assessment detection result; wherein, the facial quality assessment detection model is obtained by training based on a predetermined number of blurred human face image samples and/or clear human face image samples.

Wherein, the blurred human face image samples may include at least one of out-of-focus and blurred human face image samples, motion blurred human face image samples and poorly lit human face image samples.

It is assumed that the blurred face image samples include out-of-focus and blurred face image samples, motion blurred face image samples and insufficient light face image samples.

In this step, the face quality assessment and detection model can be obtained as follows: First, the training data roughly includes 4 types of face image samples, which are out-of-focus and blurred face image samples, and motion blurred face image samples , low-light face image samples and clear face image samples, respectively select one thousand images under the same category; then, get the face Quality assessment detection model. Wherein, how to obtain a face quality assessment and detection model through training of 1,000 face image samples of each of the 4 categories belongs to the prior art, and will not be described in detail in the embodiments of the present invention.

In the embodiment of the present invention, the facial quality assessment and detection model is obtained by training a predetermined number of fuzzy human face image samples and/or clear human face image samples, and then the human face image is used as the input of the facial quality assessment and detection model , to obtain the output face quality evaluation detection result, and determine whether there are factors such as insufficient light, motion or out-of-focus in the face image according to the face quality evaluation detection result, effectively avoiding the collected face image from being affected by the above factors The impact of the face image, thereby ensuring the subsequent success rate of using the face image for user identity authentication and face payment.

Optionally, as an embodiment, step 110 may specifically be implemented as: the first step, determine that the collected face image is located on the display interface of the terminal device In the viewfinder frame of the face; in the second step, if the proportion of the area where the face image in the viewfinder is located in the entire display interface meets the threshold, then determine that the face image is the face image to be recognized .

The threshold can be set according to actual requirements, and is not specifically limited in this embodiment of the present invention. The threshold may be the same as or different from the threshold described in the foregoing embodiments.

During specific implementation, the first step can be specifically implemented as: obtaining a face detection model based on face image sample training in advance; using the face image as the input of the face detection model to obtain the output face detection result; if the If the face detection result is normal, it is determined that the face image is located in the viewfinder of the display interface on the terminal device; if the face detection result is abnormal, the user is reminded to perform the operation of re-collecting the face image.

Exemplarily, if the face detection result is the area coordinates of the face image, it is judged whether the area coordinates fall into the coordinate set corresponding to the preset viewfinder frame; if so, it is determined that the face image is located on the terminal device If not, the user is reminded to put the face in the viewfinder and perform the operation of recapturing the face image, as shown in Figure 2.

Before executing the second step, it also includes; the third step is to obtain the area coordinates of the area where the human face image is located; the fourth step is to determine the human face based on the area coordinates and the size of the entire display interface The proportion of the area where the image is located in the entire display interface.

What needs to be added here is that if the face image in the viewfinder is located If the ratio of the area to the entire display interface does not meet the threshold, the user is reminded to perform an adjustment operation.

In the embodiment of the present invention, by determining that the collected face image is located in the viewing frame of the display interface on the terminal device, if the proportion of the area where the face image in the viewing frame is located in the entire display interface satisfies the threshold, then determine the face image The image is the face image to be recognized, which provides a prerequisite for the subsequent specified detection operation on the face image, and ensures the quality of the face image to be recognized.

Optionally, as an embodiment, when the detection result of the specified detection operation is normal, the face recognition method provided in the embodiment of the present invention may further include: performing the next specified detection operation after the specified detection operation, which may be It is understood that, when the reflection detection result corresponding to the glasses detection is normal, the occlusion detection can be performed; when the occlusion detection result corresponding to the occlusion detection is normal, the face quality evaluation detection can be performed. Wherein, the detection order of glasses detection, occlusion detection, and face quality evaluation detection can be arbitrary, which is not limited in this embodiment of the present invention. Alternatively, sending the face image to be recognized to the recognition terminal device may be understood as sending the face image to be recognized to the recognition terminal device when the detection result of the specified detection operation is normal. The identification terminal device can compare the face image with the pre-stored face image, and if the similarity value between the two is greater than a predetermined value, it will determine that the user identity authentication is passed and deduct money from the wallet to complete the payment operation. Wherein, the predetermined value needs to be set according to actual requirements, and is not specifically limited in this embodiment of the present invention.

Exemplarily, the recognition terminal device compares the face image with the pre-stored The comparison of the face images can be specifically realized as follows: obtaining the image information of the face area of the face image and the image information of the face area of the pre-stored face image, and combining the two images The information is compared, and based on the similar features in the two image information, the similarity value between the face image and the pre-stored face image is determined. Among them, the pre-stored face image can be the face image corresponding to the user's wallet account stored in advance in the identification terminal device, or it can be obtained from the official website system according to the user's ID number corresponding to the user's wallet account. face image.

In the embodiment of the present invention, when the detection result of the specified detection operation is normal, the next specified detection operation after the specified detection operation is performed, effectively eliminating the influencing factors existing in the face image to be recognized, and ensuring the detection of the face to be recognized. The quality of the image provides a guarantee for the subsequent use of face images for user identity authentication and the success rate of face payment.

In addition, when the detection result of the specified detection operation is normal, the face image to be recognized is sent to the recognition terminal device, and the recognition terminal device performs user identity authentication and face payment based on the face image to be recognized, ensuring the adoption of The success rate of face image authentication and face payment.

The method in the embodiment of the present invention will be further described below in combination with specific embodiments.

Fig. 3 shows the flow chart of the face recognition method provided by the embodiment of the present invention in an actual application scenario; Fig. 4 shows a system block diagram of the face recognition method provided by the embodiment of the present invention in an actual application scenario; example Specifically, the user's face recognition logs in to the user's wallet account for face payment, as shown in Figure 3 and Figure 4:

At 310, the terminal device 1 prompts the user to input the user's mobile phone number. After the user enters the mobile phone number on the terminal device 1, the terminal device 1 sends the user's mobile phone number to the identification terminal device.

At 320, the identification terminal device 2 receives the user's mobile phone number, and searches for the user's wallet account based on the user's mobile phone number, and if found, executes step 330; otherwise, executes step 340.

At 340, the identification terminal device 2 prompts the user for new user registration.

At 330, the terminal device 1 collects a face image.

At 350, the terminal device 1 determines whether the face image is a face image to be recognized; if yes, execute step 360; if not, execute step 330.

Wherein, the terminal device 1 determines whether the face image is a face image to be recognized, and the specific implementation may refer to relevant content in the above-mentioned embodiments, and the embodiments of the present invention will not be repeated.

At 360, the terminal device 1 performs a specified detection operation on the face image, and the specified detection operation is glasses detection. When the detection result of the designated detection operation is abnormal, execute step 361; when the detection result of the designated detection operation is normal, execute step 370 or 390.

At 361, the terminal device 1 performs a reminding operation matching the detection result, for example, reminding the user to take off the glasses.

At 370, the terminal device 1 performs a designated detection operation on the face image, and the designated detection operation is occlusion detection. When the detection result of the designated detection operation is abnormal, execute step 371; when the detection result of the designated detection operation is normal, execute step 380 or 390.

At 371, the terminal device 1 performs a reminder operation matching the detection result, Exemplarily, the user is reminded to remove the occlusion.

At 380, the terminal device 1 performs a designated detection operation on the face image, and the designated detection operation is a face quality evaluation detection. When the detection result of the specified detection operation is abnormal, perform step 381; when the detection result of the specified detection operation is normal, perform step 390.

At 381, the terminal device 1 performs a reminder operation matching the detection result, for example, reminds to adjust the image acquisition angle.

At 390, the recognition terminal device 2 receives the face image to be recognized sent by the terminal device 1, and compares the face image with the pre-stored face image; if the similarity between the two is greater than a predetermined value, Then go to step 391; if not, go to step 330.

At 391, the user identity authentication is passed and the payment operation is completed by deducting money from the wallet.

In the embodiment of the present invention, a specified detection operation is performed on the face image, and the specified detection operation includes at least one of glasses detection, occlusion detection, and face quality evaluation detection. When the detection result of the specified detection operation is abnormal, the factors affecting the face image can be identified, and a reminder operation matching the detection result is executed, so that the user can adjust according to the reminder to eliminate the factors affecting the face image, ensuring The subsequent use of face images for user identity authentication and the success rate of face payment.

In addition, when the detection result of the specified detection operation is abnormal, the reminder operation matching the detection result can be executed, which can accurately guide the user to reduce or even remove the influencing factors affecting the face image, so as to ensure that subsequent users can successfully complete the entire face payment process. Improve the pass rate of the whole link. It is also helpful It is a process for users to learn to use face payment, so that after users feel the wisdom of face payment, it will also benefit the popularization of face payment because of its unique user experience.

Above, the face recognition method of the embodiment of the present invention is described in detail with reference to FIG. 1 to FIG. 4 . Next, the terminal device of the embodiment of the present invention is described in detail with reference to FIG. 5 .

FIG. 5 shows a schematic structural diagram of a terminal device provided by an embodiment of the present invention. As shown in FIG. 5 , the terminal device 500 may include: an acquisition module 510 for acquiring a face image to be recognized; a first execution module The group 520 is used to perform a specified detection operation on the face image, and the specified detection operation includes at least one of glasses detection, occlusion detection and face quality evaluation detection; the second execution module 530 is used for when the specified detection operation When the detection result of the specified detection operation is abnormal, a reminder operation matching the detection result is executed.

In one embodiment, if the specified detection operation is glasses detection, the first execution module 520 may include: a first input unit, configured to use the human face image as an input of the reflection detection model to Obtain the output reflection detection result; wherein, the reflection detection model is obtained by training based on a predetermined number of reflection face image samples and/or non-reflection face image samples.

In one embodiment, the face image samples with reflections include at least one of face image samples with reflections from glasses and face image samples with glasses with black borders; the face images without reflections Samples include Depp At least one of face image samples without glasses and face image samples without glasses.

In one embodiment, if the specified detection operation is occlusion detection, the first execution module 520 may include: a second input unit, configured to use the human face image as an input of the occlusion detection model to An output occlusion detection result is obtained; wherein, the occlusion detection model is trained based on a predetermined number of occlusion face image samples and/or non-occlusion face image samples.

In one embodiment, the face image samples with occlusion include face image samples with hands covering faces, face image samples with bangs covering faces, and face image samples with hats covering faces and at least one of face image samples in which the face is blocked by a mask.

In one embodiment, if the specified detection operation is face quality evaluation detection, the first execution module 520 may include: a third input unit, configured to use the human face image as a facial quality evaluation The input of the detection model to obtain the output face quality assessment detection result; wherein, the facial quality assessment detection model is obtained based on a predetermined number of fuzzy face image samples and/or clear face image samples training of.

In one embodiment, the blurred human face image samples include at least one of out-of-focus blurred human face image samples, motion blurred human face image samples, and poorly lit human face image samples.

In one embodiment, the acquiring module 510 may include: a first determining unit, configured to determine that the captured face image is located in the viewfinder frame of the display interface on the terminal device; a second determining unit, configured if the If the proportion of the region where the face image in the viewfinder is located in the entire display interface satisfies a threshold, then the face image is determined to be the face image to be recognized.

In one embodiment, the acquisition module 510 may further include: an acquisition unit, configured to acquire the area coordinates of the area where the face image is located; a third determination unit, configured to obtain the area coordinates based on the area coordinates and the entire The size of the display interface determines the proportion of the area where the face image is located in the entire display interface.

In an embodiment, the terminal device may further include: a third execution module 540, configured to execute the next designated detection operation after the designated detection operation when the detection result of the designated detection operation is normal; or , a sending module 550, configured to send the face image to be recognized to the recognition terminal device.

In the embodiment of the present invention, a specified detection operation is performed on the face image, and the specified detection operation includes at least one of glasses detection, occlusion detection, and face quality evaluation detection. When the detection result of the specified detection operation is abnormal, the factors affecting the face image can be identified, and a reminder operation matching the detection result is executed, so that the user can adjust according to the reminder to eliminate the factors affecting the face image, ensuring The subsequent use of face images for user identity authentication The success rate of card and face payment.

In addition, when the detection result of the specified detection operation is abnormal, the reminder operation matching the detection result can be executed, which can accurately guide the user to reduce or even remove the influencing factors affecting the face image, so as to ensure that subsequent users can successfully complete the entire face payment process. Improve the pass rate of the whole link. At the same time, it is also a process to help users learn to use face payment, so that after users feel the wisdom of face payment, it will also benefit the popularization of face payment because of its unique user experience.

Fig. 6 is a schematic structural diagram of a terminal device provided by an embodiment of this specification. Please refer to FIG. 6 , at the hardware level, the terminal device includes a processor, and optionally also includes an internal bus, a network interface, and a memory. Wherein, the memory may include memory, such as high-speed random access memory (Random-Access Memory, RAM), and may also include non-volatile memory (non-volatile memory), such as at least one disk memory Wait. Of course, the terminal equipment may also include hardware required by other services.

The processor, network interface, and memory can be connected to each other through an internal bus, which can be an ISA (Industry Standard Architecture, industry standard architecture) bus, a PCI (Peripheral Component Interconnect, peripheral component interconnection standard) bus row or EISA (Extended Industry Standard Architecture, extended industry standard architecture) busbar, etc. The bus can be divided into address bus, data bus, control bus and so on. For ease of representation, only one double-headed arrow is used in FIG. 6 , but it does not mean that there is only one bus bar or one type of bus bar.

Memory, used to store programs. Specifically, the program may include program code, and the program code includes computer operation instructions. Memory, which can include both internal and non-volatile memory, provides instructions and data to the processor.

The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it, forming an association device between resource value-added objects and resource objects on a logical level. The processor executes the program stored in the memory, and is specifically used to perform the following operations: acquire a face image to be recognized; perform a designated detection operation on the face image, and the designated detection operation includes glasses detection, occlusion At least one of detection and face quality evaluation detection; when the detection result of the specified detection operation is abnormal, perform a reminder operation matching the detection result.

In the embodiment of the present invention, a specified detection operation is performed on the face image, and the specified detection operation includes at least one of glasses detection, occlusion detection, and face quality evaluation detection. When the detection result of the specified detection operation is abnormal, the factors affecting the face image can be identified, and a reminder operation matching the detection result is executed, so that the user can adjust according to the reminder to eliminate the factors affecting the face image, ensuring The subsequent use of face images for user identity authentication and the success rate of face payment.

In addition, when the detection result of the specified detection operation is abnormal, the reminder operation matching the detection result can be executed, which can accurately guide the user to reduce or even remove the influencing factors affecting the face image, so as to ensure that subsequent users can successfully complete the entire face payment process. Improve the pass rate of the whole link. It is also helpful It is a process for users to learn to use face payment, so that after users feel the wisdom of face payment, it will also benefit the popularization of face payment because of its unique user experience.

The above face recognition method disclosed in the embodiment shown in FIG. 1 of this specification may be applied to or implemented by a processor. A processor may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by an integrated logic circuit of the hardware in the processor or an instruction in the form of software. The above-mentioned processor can be a general-purpose processor, including a central processing unit (Central Processing Unit, CPU), a network processor (Network Processor, NP), etc.; it can also be a digital signal processor (Digital Signal Processor, DSP), a dedicated Integrated circuits (Application Specific Integrated Circuit, ASIC), Field-Programmable Gate Array (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. The various methods, steps and logic block diagrams disclosed in one or more embodiments of this specification can be implemented or executed. A general-purpose processor may be a microprocessor, or the processor may be any conventional processor, and the like. The steps of the method disclosed in conjunction with one or more embodiments of this specification can be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module can be located in a mature storage medium in the field such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically readable and writable programmable memory, and scratchpad. The storage medium is located in the memory, and the processor reads the information in the memory, and combines with its hardware to complete the steps of the above method.

The terminal device can also execute the face recognition method shown in FIG. 1 , which will not be repeated here in this specification.

Of course, in addition to the software implementation, the terminal equipment in this specification does not exclude other implementations, such as logic devices or the combination of software and hardware, etc., that is to say, the execution subject of the following processing flow is not limited to each logic A unit, which can also be a hardware or logic device.

The embodiment of this specification also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, each process of the above-mentioned method embodiments can be achieved, and the same technical effect can be achieved. To avoid repetition, details are not repeated here. Wherein, the computer-readable storage medium is, for example, a read-only memory (Read-Only Memory, ROM for short), a random access memory (Random Access Memory, RAM for short), a magnetic disk or an optical disk, and the like.

Those skilled in the art should understand that the embodiments of the present invention may be provided as methods, systems, or computer program products. Accordingly, the present invention can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk memory, CD-ROM, optical memory, etc.) having computer-usable program code embodied therein .

The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It should be understood that each process and/or block in the flowchart and/or block diagram, and the processes and processes in the flowchart and/or block diagram can be realized by computer program instructions. / or a combination of boxes. These computer program instructions can be provided to the processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing equipment to produce a machine so that the instructions executed by the processor of the computer or other programmable data processing equipment Produce a system for realizing the functions specified in one or more procedures of the flowchart and/or one or more blocks of the block diagram.

These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, such that the instructions stored in the computer readable memory produce an article of manufacture including a system of instructions , the instruction system implements the functions specified in one or more procedures of the flow chart and/or one or more blocks of the block diagram.

These computer program instructions may also be loaded into a computer or other programmable data processing device, causing a series of operational steps to be performed on the computer or other programmable device to produce computer-implemented The instructions executed above provide steps for implementing the functions specified in one or more procedures of the flowchart and/or one or more blocks of the block diagram.

In a typical configuration, a computing device includes one or more processors (CPU), input/output interfaces, network interfaces, and memory.

Memory may include non-permanent memory in computer readable media, random access memory - RAM) and/or nonvolatile memory such as read only memory - ROM) or flash memory ( flash RAM). The memory is an example of a computer readable medium.

Computer-readable media includes both permanent and non-permanent, removable and non- Removable media can be implemented by any method or technology for information storage. Information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for computers include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM) , Read-Only Memory-ROM), Electrically Erasable Programmable Read-Only Memory-EEPROM), Flash memory or other memory technologies, Compact Disc-Read-Only Memory-CD-ROM), Digital Multifunction Compact Disc - DVD) or other optical storage, magnetic cassette, magnetic tape magnetic disk storage or other magnetic storage device or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media (transitory media), such as modulated data signals and carrier waves.

It should also be noted that the term "comprises", "comprises" or any other variation thereof is intended to cover a non-exclusive inclusion such that a process, method, article, or apparatus comprising a set of elements includes not only those elements, but also includes Other elements not expressly listed, or elements inherent in the process, method, commodity, or apparatus are also included. Without further limitations, an element qualified by the phrase "comprising a..." does not preclude the presence of additional identical elements in the process, method, article, or apparatus that includes the element.

The above are only examples of the present invention, and are not intended to limit the present invention. Various modifications and variations of the present invention will occur to those skilled in the art. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the patent scope of the present invention Inside.

Claims (9)

A face recognition method, comprising: determining that the collected face image is located in a viewfinder frame of a display interface on a terminal device; obtaining the area coordinates of the area where the face image is located; based on the area coordinates and the size of the entire display interface , determine the proportion of the area where the face image is located in the entire display interface; if the proportion of the area where the face image in the viewfinder is located in the entire display interface meets the threshold, then determine that the face image is to be Recognized face image; if the proportion of the area where the face image in the viewfinder is located in the entire display interface does not meet the threshold, remind the user to be recognized to perform an adjustment operation; perform a specified detection operation on the face image , the specified detection operation includes at least one of glasses detection, occlusion detection, and face quality evaluation detection; when the detection result of the specified detection operation is abnormal, a reminder operation matching the detection result is executed, and when the specified detection operation When the detection result is normal, the face image to be recognized is sent to the recognition terminal device, and the recognition terminal device is used to compare the face image with a pre-stored face image of a user, and according to the comparison Authenticate the user on the result, and when the identity authentication is passed, debit the money from the wallet to complete the payment operation. acquired by way of shooting, Wherein, if the designated detection operation is glasses detection, performing the designated detection operation on the face image includes: using the face image as an input of the reflective detection model to obtain an output reflective detection result; wherein, the reflective The detection model is trained based on a predetermined number of reflective face image samples and/or non-reflective face image samples, wherein, if the specified detection operation is face quality evaluation detection, the face image Like performing a specified detection operation, including: using the face image as an input of a facial quality assessment detection model to obtain an output facial quality assessment detection result; wherein, the facial quality assessment detection model is based on a predetermined amount of blur face image samples and/or clear face image samples for training. According to the method described in claim 1, the face image samples with reflections include at least one of face image samples with reflections from glasses and face image samples with glasses with black borders; The face image samples include at least one of face image samples wearing ordinary glasses and face image samples without glasses. According to the method described in claim 1, if the specified detection operation is occlusion detection, performing the specified detection operation on the face image includes: using the face image as an input of the occlusion detection model to obtain an output occlusion Test results; Wherein, the occlusion detection model is trained based on a predetermined number of occluded face image samples and/or non-occluded face image samples. According to the method described in claim 3, the face image samples with occlusion include face image samples with hands covering faces, face image samples with bangs covering faces, and faces with hats covering faces At least one of an image sample and a face image sample in which a face is blocked by a mask. According to the method described in claim 1, the blurred face image samples include at least one of out-of-focus and blurred face image samples, motion blurred face image samples, and insufficiently lighted face image samples. The method as described in claim 1, the method further includes: when the detection result of the specified detection operation is normal, performing the next specified detection operation after the specified detection operation; or, converting the face image to be recognized Sent to the identification terminal device. A terminal device, comprising: an acquisition module, used to determine that the collected face image is located in the viewfinder frame of the display interface on the terminal device; obtain the area coordinates of the area where the face image is located; based on the area coordinates and the entire described The size of the display interface determines the proportion of the area where the face image is located in the entire display interface; if the proportion of the area where the face image in the viewfinder is located in the entire display interface meets the threshold, then determine the face The image is a face image to be recognized; if the viewfinder If the proportion of the area where the face image is located in the entire display interface does not meet the threshold, the user to be recognized will be reminded to perform an adjustment operation; the first execution module is used to perform a specified detection operation on the face image, and the specified The detection operation includes at least one of glasses detection, occlusion detection, and face quality evaluation detection; the second execution module is used to execute a reminder operation that matches the detection result when the detection result of the specified detection operation is abnormal. When the detection result of the specified detection operation is normal, the face image to be recognized is sent to the recognition terminal device, and the recognition terminal device is used to compare the face image with a pre-stored face image of a user Yes, the user is authenticated according to the comparison result, and when the identity authentication is passed, the payment operation is completed by deducting money from the wallet. The user is the user corresponding to the wallet account; the face image to be identified is Obtained by methods including scanning or photographing, wherein, if the specified detection operation is glasses detection, performing a specified detection operation on the face image includes: using the face image as an input of the reflective detection model to obtain Output reflective detection results; wherein, the reflective detection model is trained based on a predetermined number of reflective face image samples and/or non-reflective face image samples, wherein, if the specified detection operation is a face Quality assessment detection, performing a specified detection operation on the face image, including: using the face image as an input of a face quality assessment detection model to obtain an output face quality assessment detection result; Wherein, the facial quality assessment detection model is obtained by training based on a predetermined number of blurred human face image samples and/or clear human face image samples. A terminal device, including: a memory, a processor, and a computer program stored on the memory and operable on the processor. When the computer program is executed by the processor, the following steps are implemented: determine the collected face image Located in the viewfinder frame of the display interface on the terminal device; obtaining the area coordinates of the area where the face image is located; based on the area coordinates and the size of the entire display interface, determining that the area where the face image is located is within the entire display interface If the proportion of the area where the face image in the viewfinder is located in the entire display interface meets the threshold, then it is determined that the face image is the face image to be recognized; if the face in the viewfinder If the proportion of the area where the image is located in the entire display interface does not meet the threshold, the user to be recognized is reminded to perform an adjustment operation; the specified detection operation is performed on the face image, and the specified detection operation includes glasses detection, occlusion detection and face quality Evaluate at least one of the detections; when the detection result of the specified detection operation is abnormal, perform a reminder operation that matches the detection result, and when the detection result of the specified detection operation is normal, send the face image to be recognized to a recognition terminal device, which is used to compare the face image with a pre-stored face image of a user, authenticate the user according to the comparison result, and When the identity authentication is passed, the payment operation is completed by deducting money from the wallet, and the user is the user corresponding to the account number of the wallet; wherein, the face image to be recognized is obtained by means including scanning or photographing, wherein, if The specified detection operation is glasses detection, and then the specified detection operation is performed on the face image, including: using the face image as the input of the reflection detection model to obtain the output reflection detection result; wherein, the reflection detection model is It is obtained based on a predetermined number of face image samples with reflections and/or face image samples without reflections, wherein, if the specified detection operation is face quality evaluation detection, the specified detection operation is performed on the face image The detection operation includes: using the face image as an input of a facial quality assessment detection model to obtain an output facial quality assessment detection result; wherein, the facial quality assessment detection model is based on a predetermined number of blurred human faces image samples and/or clear face image samples for training. A computer-readable storage medium. A computer program is stored on the computer-readable storage medium. When the computer program is executed by a processor, the following steps are implemented: determining that the collected face image is located in the viewfinder frame of the display interface on the terminal device; acquiring The area coordinates of the area where the face image is located; based on the area coordinates Mark and the size of the entire display interface, determine the proportion of the area where the face image is located in the entire display interface; if the proportion of the area where the face image in the viewfinder is located in the entire display interface meets the threshold, Then it is determined that the face image is the face image to be recognized; if the ratio of the area where the face image in the viewfinder is located in the entire display interface does not meet the threshold, the user to be recognized is reminded to perform an adjustment operation; The face image performs a designated detection operation, the designated detection operation includes at least one of glasses detection, occlusion detection and face quality evaluation detection; when the detection result of the designated detection operation is abnormal, a reminder matching the detection result is executed Operation, when the detection result of the specified detection operation is normal, the face image to be recognized is sent to the recognition terminal device, and the recognition terminal device is used to compare the face image with a pre-stored face of a user The image is compared, and the user is authenticated according to the comparison result, and when the identity authentication is passed, the payment is deducted from the wallet to complete the payment operation. The user is the user corresponding to the account of the wallet; the person to be identified The face image is obtained through methods including scanning or photographing, wherein, if the specified detection operation is glasses detection, the specified detection operation is performed on the face image, including: using the face image as a reflection detection model input to obtain the output reflection detection result; wherein, the reflection detection model is obtained based on a predetermined number of reflection face image samples and/or non-reflection face image samples training wherein, if the specified detection operation is face quality assessment detection, then performing the specified detection operation on the face image includes: using the face image as the input of the face quality assessment detection model to obtain the output Facial quality assessment detection results; wherein, the facial quality assessment detection model is trained based on a predetermined number of blurred human face image samples and/or clear human face image samples.

Images