KR20230042829A - Electronic device for recommending beauty styling to user and method for operation thereof - Google Patents

Abstract

One embodiment of the present invention relates to an electronic device that recommends a makeup style and hairstyle to a user based on a user's face image and a user's hair image and an operating method thereof. Since each user has a different face type or skin tone, and has a different hair type or hair color, it is difficult for even a styling expert to always recommend styling that is suitable for the individual. In addition, even among users with similar face types, skin tones, hair types, and hair colors, preferred styling methods may differ depending on age, so it is necessary to objectively consider various information and recommend styling suitable for the user. An electronic device and an operating method thereof according to an embodiment of the present invention can objectively determine a user's face type and user hair type by applying the user's face image and user hair image to a deep learning model and provide a recommended makeup style and a recommended hairstyle suitable for the user by additionally considering the age information, facial skin tone, and hair color of the user in addition to the determined face type and determined hair type.

Description

Electronic device recommending beauty styling to users and its operation method {ELECTRONIC DEVICE FOR RECOMMENDING BEAUTY STYLING TO USER AND METHOD FOR OPERATION THEREOF}

The present invention relates to an apparatus and system for providing beauty-related services.

An embodiment of the present invention also relates to an electronic device that recommends a makeup style and a hairstyle to a user based on a user's face image and a user's hair image, and an operating method thereof.

With the development of the beauty industry, users' interest in cosmetics, makeup, and the like is increasing. Accordingly, it is a trend that users' desires for cosmetics, makeup, and the like are diversifying. For example, since each user has a variety of skin colors, face shapes, features, etc., makeup suitable for each individual may be different. Therefore, the user may have difficulty in selecting makeup suitable for him or her. In addition, the user may wonder whether the makeup is well done or what part needs to be supplemented after the makeup.

In line with this trend, recently, applications that recommend beauty styling have been developed, but popular styling methods are provided to users regardless of each user's skin tone, face type, hair type, and hair color. I am recommending

(001) Korean Patent Publication No. 10-2015-0098130 (2015.08.27) (002) Korean Patent Publication No. 10-2018-0126664 (2018.10.23)

Since each user has a different face type or skin tone, as well as different hair types and hair colors, it is difficult to always recommend styling suitable for each individual even if they are experts in styling. In addition, even among users with similar face types, skin tones, hair types, and hair colors, preferred styling methods may differ according to age groups, so it is necessary to objectively consider various information to recommend styling suitable for users.

An electronic device and an operating method thereof according to an embodiment of the present invention apply a user face image and a user hair image to a deep learning model to objectively determine a user face type and a user hair type, as well as determine the determined face type and A recommended makeup style and a recommended hair style suitable for the user may be provided by additionally considering the user's age information, facial skin tone, and hair color in addition to the determined hair type.

The technical problems to be achieved in the present invention are not limited to the technical problems mentioned above, and other technical problems not mentioned will be clearly understood by those skilled in the art from the description below. You will be able to.

According to an embodiment, an electronic device includes a display and a processor, wherein the processor obtains age information of the user, a face image of the user, and an image of the user's hair from a user, and displays a facial part in the user's face image. A CNN-based first deep learning model for determining a plurality of first features and facial skin tone, determining a plurality of second features and hair color for a hair state in the user's hair image, and determining a face type. The user's face type is determined among a plurality of predetermined face types by applying the plurality of first features of the face part to the hair type, and the hair type is applied to a CNN-based second deep learning model for determining the hair type. The user's hair type is determined from among a plurality of predetermined hair types by applying the plurality of second features of the state, and based on the age information, the face type, and the facial skin tone, the hair type of the user is determined. A virtual face obtained by determining a recommended makeup style, determining a recommended hairstyle for the user based on the age information, the hair type, and the hair color, and applying an effect related to the recommended makeup style to the user's face image. An image is generated, a virtual hair image is created by applying an effect related to the recommended hairstyle to the user's hair image, and the virtual face image, information on a plurality of recommended cosmetics usable for the recommended makeup style, the virtual hair image It may be set to display a hair image and information about a plurality of recommended hair shops that can perform the recommended hair style on one screen on the display.

According to an embodiment, the processor analyzes the user's face image to determine a face shape of the user as one of the plurality of first features, and to determine the user's face shape. , [Equation 1] is used to calculate the similarity between the user's face image and pre-stored face shape images, and among the previously stored face shape images, the face shape image having the highest similarity with the user's face image The face shape may be set to determine the face shape of the user.

[Equation 1]

x _i may represent a feature vector extracted from a user's face image, and y _i may represent a feature vector extracted from a previously stored face shape image.

According to an embodiment, the processor analyzes the user's face image to determine the shape of each face part as one of the plurality of first features, and to determine the shape of each face part, An edge for specifying each face part is extracted from the user's face image, and a shape corresponding to the edge for specifying each face part among predetermined shapes for each face part is the shape of each face part. It can be set to select as

According to an embodiment, the processor may be configured to analyze the user's face image and calculate a plurality of ratio information for each face part as one of the plurality of first features.

According to an embodiment, the processor analyzes the user's hair image to determine the user's hair shape as one of the plurality of second features, and analyzes the user's hair image to determine the plurality of second features. As one of the second features, it can be set to determine the user's hair width.

According to an embodiment, the processor stores the face shape of the user, the shape of each face part, and a plurality of ratio information for each face part in a first table learned by the first deep learning model. to determine the user's face type among the plurality of predetermined face types, and apply the hair shape and hair width of the user to a second table learned by the second deep learning model, It may be set to determine the user's hair type from among the plurality of predetermined hair types.

According to an embodiment of the present invention, the face type is determined based on the shape of the user's face, the shape of each part of the face, and the ratio information of each part of the face derived from the user's face image using the first deep learning model , The hair type is determined based on the user's hair shape and hair width derived from the user's hair image using the second deep learning model, and the user's face type and hair type are objectively determined, thereby providing a makeup style suitable for the user and Can you recommend a hair style? In addition to the user's face type and hair type, the user's age, skin tone of the user's face, and color of the user's hair are additionally considered to recommend a makeup style and a hair style, so that the user can easily create styling effects. there is.

The effects obtainable in the present invention are not limited to the effects mentioned above, and other effects not mentioned can be clearly understood by those skilled in the art from the description below. will be.

Also other aspects as described above, features and benefits of certain preferred embodiments of the present invention will become more apparent from the following description taken in conjunction with the accompanying drawings.
1 shows a block diagram of an electronic device and network according to one embodiment of the present invention.
2 is a block diagram of an electronic device according to an embodiment of the present invention.
3 is a block diagram of a program module according to an embodiment of the present invention.
4 is a flowchart illustrating a method of operating an electronic device according to an embodiment of the present invention.
5 illustrates an embodiment in which an electronic device obtains age information, a face image, and a hair image of a user according to an embodiment of the present invention.
6 shows a first table learned by a CNN-based first deep learning model according to an embodiment of the present invention.
7 shows a second table learned by a CNN-based second deep learning model according to an embodiment of the present invention.
8 is a virtual hair image by generating a virtual face image by applying an effect related to a recommended makeup style to a user's face image and applying an effect related to a recommended hairstyle to a user's hair image according to an embodiment of the present invention. shows an example of generating
9 illustrates a virtual face image, information on a plurality of recommended cosmetics that can be used for a recommended makeup style, a virtual hair image, and a plurality of recommended hair shops that can treat the recommended hair style, according to an embodiment of the present invention. It shows an embodiment of displaying information on one screen.

Hereinafter, an embodiment of this document will be described with reference to the accompanying drawings. Examples and terms used therein are not intended to limit the technology described in this document to specific embodiments, and should be understood to include various modifications, equivalents, and/or substitutes of the embodiments. In connection with the description of the drawings, like reference numerals may be used for like elements. Singular expressions may include plural expressions unless the context clearly dictates otherwise. In this document, expressions such as "A or B" or "at least one of A and/or B" may include all possible combinations of the items listed together. Expressions such as "first," "second," "first," or "second," may modify the corresponding components regardless of order or importance, and are used to distinguish one component from another. It is used only and does not limit the corresponding components. When a (e.g., first) element is referred to as being "(functionally or communicatively) coupled to" or "connected to" another (e.g., second) element, that element refers to the other (e.g., second) element. It may be directly connected to the component or connected through another component (eg, a third component).

In this document, "configured (or configured to)" means "suitable for," "having the ability to," "changed to," depending on the situation, for example, hardware or software. ," can be used interchangeably with "made to," "capable of," or "designed to." In some contexts, the expression "device configured to" can mean that the device is "capable of" in conjunction with other devices or components. For example, the phrase "a processor configured (or configured) to perform A, B, and C" may include a dedicated processor (eg, embedded processor) to perform the operation, or by executing one or more software programs stored in a memory device. , may mean a general-purpose processor (eg, CPU or application processor) capable of performing corresponding operations.

Electronic devices according to an embodiment of the present document include, for example, a smart phone, a tablet PC, a mobile phone, a video phone, an e-book reader, a desktop PC, a laptop PC, a netbook computer, a workstation, a server, a PDA, a PMP ( portable multimedia player), an MP3 player, a medical device, a camera, or a wearable device. A wearable device may be in the form of an accessory (e.g. watch, ring, bracelet, anklet, necklace, eyeglasses, contact lens, or head-mounted-device (HMD)), integrated into textiles or clothing (e.g. electronic garment); In some embodiments, the electronic device may include, for example, a television, a digital video disk (DVD) player, Audio, refrigerator, air conditioner, vacuum cleaner, oven, microwave, washing machine, air purifier, set top box, home automation control panel, security control panel, media box (e.g. Samsung HomeSync ^TM , Apple TV ^TM , or Google TV ^TM ) , a game console (eg, Xbox ^TM , PlayStation ^TM ), an electronic dictionary, an electronic key, a camcorder, or an electronic picture frame.

In another embodiment, the electronic device may include various types of medical devices (e.g., various portable medical measuring devices (such as blood glucose meter, heart rate monitor, blood pressure monitor, or body temperature monitor), magnetic resonance angiography (MRA), magnetic resonance imaging (MRI), CT (computed tomography), imager, or ultrasonicator, etc.), navigation device, global navigation satellite system (GNSS), EDR (event data recorder), FDR (flight data recorder), automobile infotainment device, marine electronic equipment (e.g. navigation devices for ships, gyrocompasses, etc.), avionics, security devices, head units for vehicles, industrial or home robots, drones, ATMs in financial institutions, point of sale (POS) in stores of sales), or IoT devices (eg, light bulbs, various sensors, sprinkler devices, fire alarms, thermostats, street lights, toasters, exercise equipment, hot water tanks, heaters, boilers, etc.). According to some embodiments, the electronic device may be a piece of furniture, a building/structure or a vehicle, an electronic board, an electronic signature receiving device, a projector, or various measuring devices (eg, water, electricity, gas, radio wave measuring device, etc.). In one embodiment, the electronic device may be flexible or a combination of two or more of the various devices described above. An electronic device according to an embodiment of the present document is not limited to the aforementioned devices. In this document, the term user may refer to a person using an electronic device or a device using an electronic device (eg, an artificial intelligence electronic device).

Referring to FIG. 1 , in one embodiment, an electronic device 101 in a network environment 100 is described. The electronic device 101 may include a bus 110, a processor 120, a memory 130, an input/output interface 150, a display 160, and a communication interface 170. In some embodiments, the electronic device 101 may omit at least one of the components or may additionally include other components.

A detailed description of the electronic device 101 according to an embodiment of the present invention will be described later with reference to FIGS. 1 to 3 .

4 is a flowchart illustrating a method of operating an electronic device (eg, the electronic device 101 of FIG. 1 ) according to an embodiment of the present invention.

5 illustrates an embodiment in which the electronic device 101 acquires age information, a face image, and a hair image of a user according to an embodiment of the present invention.

6 shows a first table learned by a CNN-based first deep learning model according to an embodiment of the present invention.

7 shows a second table learned by a CNN-based second deep learning model according to an embodiment of the present invention.

8 is a diagram in which the electronic device 101 generates a virtual face image by applying an effect related to a recommended makeup style to a user's face image and applies an effect related to a recommended hair style to a user's hair image according to an embodiment of the present invention. An embodiment of generating a virtual hair image is shown.

FIG. 9 shows a virtual face image, information on a plurality of recommended cosmetics that can be used for a recommended makeup style, a virtual hair image, and a plurality of information on which the recommended hair style can be applied by the electronic device 101 according to an embodiment of the present invention. An embodiment of displaying information on recommended hair shops on one screen is shown.

According to an embodiment, the electronic device 101 downloads an application that provides a service for recommending beauty styling suitable for a user from an external server (eg, the server 106 of FIG. 1 ) and installs it in the electronic device 101 . can Hereinafter, a method for the electronic device 101 to provide a service for recommending beauty styling through the application will be described.

In operation 401, according to an embodiment, the electronic device 101 (eg, the processor 120 of FIG. 1 ) obtains the user's age information, the user's face image, and the user's hair image from the user. For example, referring to FIG. 5 , the electronic device 101 may receive age information 501 from the user, and provide a user's face image 511 in which the user's face is photographed so that the user's face comes out. A user's hair image 521 in which the back of the user is photographed so that the hair shape appears may be obtained.

In operation 403, according to an embodiment, the electronic device 101 (eg, the processor 120 of FIG. 1 ) determines a plurality of first features and facial skin tone of a facial part in the user's face image 511, , a plurality of second features of the hair condition in the user's hair image 521 and the hair color may be determined.

According to an embodiment, the electronic device 101 may analyze the user's face image 511 and determine the user's face shape as one of the plurality of first features. For example, the electronic device 101 uses the following [Equation 1] to obtain the user's face image 511 and pre-stored face shape images (eg, egg, round, square, diamond, inverted triangle, etc.) The similarity of may be calculated, and a face shape image (eg, egg shape) having the highest similarity to the user's face image 511 among the previously stored face shape images may be determined as the user's face shape.

[Equation 1]

x _i denotes a feature vector extracted from the user's face image 511, and y _i denotes a feature vector extracted from a previously stored face shape image.

According to an embodiment, the electronic device 101 may analyze the user's face image 511 and determine the shape of each face part as one of the plurality of first features. According to an embodiment, the electronic device 101 may extract an edge specifying each face part from the user's face image 511 and determine a shape corresponding to the edge of the face part. For example, the electronic device 101 may extract an edge specifying a first facial part (eg, an eyebrow), and may have predetermined shapes (eg, a1, a2, a3, a4, etc.) ), a first shape (eg, a2) corresponding to the edge of the first facial part may be selected. As another example, the electronic device 101 may extract an edge specifying a second face part (eg, an eye), and may have predetermined shapes (eg, b1, b2, b3, b4, etc.), a second shape (eg, b1) corresponding to the edge of the second facial part may be selected. As another example, the electronic device 101 may extract an edge specifying a third facial part (eg, lips), and may extract predetermined shapes (eg, c1, c2, c3, c4, etc.), a third shape (eg, c3) corresponding to the edge of the third facial part may be selected.

According to an embodiment, the electronic device 101 may analyze the user's face image 511 and calculate a plurality of ratio information for each face part as one of the plurality of first features. According to an embodiment, the electronic device 101 may calculate a plurality of ratio information for each face part based on an edge specifying each face part. Hereinafter, 'length' means the largest value among edge values measured horizontally, and 'width' means the largest value among edge values measured vertically. For example, the electronic device 101 may provide a first ratio (eg, a ratio of eyebrow length to eyebrow width) and a second ratio with respect to the first facial part (eg, eyebrow width) based on an edge specifying the first facial part (eg, eyebrow). A ratio (eg eyebrow length to face length ratio), eg a third ratio (eg eyebrow length to lip length ratio) may be calculated. As another example, the electronic device 101 may provide a first ratio (eg, eye length to eye width ratio) with respect to the second facial part, based on an edge specifying the second facial part (eg, eyes); A second ratio (eg, ratio of eye width to forehead width) and a third ratio (eg, ratio of eye length to lip length) may be calculated. For another example, the electronic device 101 may set a first ratio (eg, a ratio of a lip length to a lip width) with respect to the third facial part based on an edge specifying the third facial part (eg, lips) and A second ratio (eg, ratio of lip length to nose length) can be calculated.

According to an embodiment, the electronic device 101 may analyze the user's face image 511 to determine the skin tone level of the user's face. For example, the electronic device 101 sets the color level corresponding to the skin tone of the user's face between the first level (eg, brightest white) to the twentieth level (eg, darkest black) (eg, 3 levels (lightest flesh color)) can be determined.

According to an embodiment, the electronic device 101 may analyze the user's hair image 521 and determine the user's hair shape as one of the plurality of second features. For example, the electronic device 101 may extract the edge of the user's hair from the user's hair image 521, and correspond to the edge of the user's hair among predetermined hair types (eg, straight hair, semi-curly hair, fully curly hair, etc.) You can decide what type of hair you want (e.g. semi-curly).

According to an embodiment, the electronic device 101 may analyze the user's hair image 521 and calculate the user's hair width as one of the plurality of second features. For example, the electronic device 101 may extract the edge of the user's hair from the user's hair image 521, and determine the width of the edge of the user's hair among predetermined hair widths (eg, short cut, short hair, long hair, etc.) A width (eg, short hair) of a corresponding user's hair may be determined.

According to an embodiment, the electronic device 101 may determine the color of the user's hair by analyzing the user's hair image 521 . For example, the electronic device 101 sets the color level corresponding to the color of the user's hair between the first level (eg, the brightest white) to the 30th level (eg, the darkest black) (eg, the 15th level (the darkest black)). brown)) can be determined.

In operation 405, according to an embodiment, the electronic device 101 (eg, the processor 120 of FIG. 1 ) assigns a convolutional neural network (CNN)-based first deep learning model to a face part for determining a face type. Determines the user's face type from among a plurality of predetermined face types by applying a plurality of first features to the plurality of second features of the hair state to a CNN-based second deep learning model for determining the hair type. , it is possible to determine the user's hair type from among a plurality of predetermined hair types.

According to an embodiment, the electronic device 101 learns the shape of the user's face, the shape of each facial part of the user, and a plurality of ratio information for each facial part of the user by a CNN-based first deep learning model. By applying the first table, the user's face type may be determined from among a plurality of predetermined face types. For example, referring to FIG. 6 , the electronic device 101 determines the user's face shape (e.g. (a) = egg shape), the determined user's face shape (e.g. (b) = a1, (da) ) = b1, (d) = c2), a plurality of ratio information for each facial part of the determined user (e.g., (e) = a first value belonging to the first range, (f) = a second value belonging to the first range value, (g) = a third value belonging to the first range) is applied to the first table learned by the CNN-based first deep learning model, and the user's A first face type 601 as a face type may be determined.

According to an embodiment, the electronic device 101 applies the user's hair shape and the user's hair width to a second table learned by a CNN-based second deep learning model to select among a plurality of predetermined hair types. The user's hair type may be determined. For example, referring to FIG. 7 , the electronic device 101 determines the determined user's hair shape (eg, (a) = semi-curly) and the determined user's hair width (eg, (b) = short hair) based on CNN. By applying the second table learned by the second deep learning model, the second hair type 701, which is the user's hair type, may be determined from among the first to Kth hair types.

According to an embodiment, the electronic device 101 may receive an updated first deep learning model and an updated first table from the external server 106 .

According to an embodiment, the external server 106 may train a first deep learning model by additionally learning a plurality of face images, and as training of the first deep learning model progresses, each element in the first table can be updated For example, the external server 106 may receive a face type corresponding to each face image along with a plurality of face images, and learn the face shape and face for each face type by learning the plurality of face images. It is possible to match the shape of the part and a plurality of ratio information about the face part. According to an embodiment, the electronic device 101 may receive an updated second deep learning model and an updated second table from the external server 106 .

According to an embodiment, the external server 106 may train a second deep learning model by additionally learning a plurality of hair images, and as the training of the second deep learning model progresses, each element in the second table can be updated For example, the external server 106 may receive a plurality of hair images and a hair type corresponding to each hair image, and by learning the plurality of hair images, the hair shape and hair type for each hair type. width can be matched

A plurality of hair images may be additionally learned to train the second deep learning model, and as training of the second deep learning model progresses, each element in the second table may be updated.

In operation 407, according to an embodiment, the electronic device 101 (eg, the processor 120 of FIG. 1 ) determines the user's age information 501, the user's face type, and the user's face skin tone. A recommended makeup style for the user may be determined, and a recommended hair style for the user may be determined based on the user's age information 501, the user's hair type, and the user's hair color. This is to recommend a makeup style suitable for the user's age, face type, and skin tone to the user, as the preferred makeup style may differ depending on the user's age and skin tone even if the user's face type is the same. This is to recommend a hairstyle suitable for the user's age, hair type, and hair color to the user because the preferred hairstyle may be different depending on the user's age and hair color even if the type is the same.

According to an embodiment, the electronic device 101 stores a makeup style table defining parameter values related to effects of makeup styles corresponding to each age, each face type, and each facial skin tone level (eg, the memory of FIG. 1 ). 130)), and parameter values of recommended makeup styles corresponding to age information 501 of the user, face type, and skin tone of the user can be checked from the makeup style table. For example, the electronic device 101 includes information about the user's age 501 (eg, a teenager), a face type of the user (eg, a first face type 601), and a skin tone level of the user's face (eg, 3 Parameter values (for example, values related to makeup area and makeup color for each face part) related to the effect of the recommended makeup style uniquely specified by the makeup style table may be checked from the makeup style table.

According to an embodiment, the makeup style table may store descriptions for presenting each makeup style together with each parameter value.

According to an embodiment, the electronic device 101 may store, in the memory 130, a hair style table defining parameter values related to effects of hair styles corresponding to each age, each hair type, and each hair color. A recommended hairstyle corresponding to the user's age information 501, hair type, and hair color may be determined from the hair style table. For example, the electronic device 101 may provide information about the user's age 501 (eg, teenager), the user's hair type (eg, second hair type 701), and the user's hair color level (eg, 5 Parameter values related to the effect of the recommended hair style, which are uniquely specified by the level), can be checked from the hair style table.

According to an embodiment, the hair style table may store descriptions for creating each hair style together with each parameter value.

In operation 409, according to an embodiment, the electronic device 101 (eg, the processor 120 of FIG. 1) generates a virtual face image by applying an effect related to the recommended makeup style to the user's face image 511, and recommends A virtual hair image may be generated by applying a hair style effect to the user's hair image 521 . For example, referring to <801> of FIG. 8, the electronic device 101 generates a virtual face image 811 obtained by applying parameter values related to the effect of the recommended makeup style identified from the makeup style table to the user's face image 511. can create For example, referring to <802> of FIG. 8 , the electronic device 101 generates a virtual hair image 821 obtained by applying a parameter value related to an effect of a recommended hairstyle identified from a hair style table to a user hair image 521. can create

In operation 411, according to an embodiment, the electronic device 101 (eg, the processor 120 of FIG. 1 ) displays the virtual face image 811, information about a plurality of recommended cosmetics that can be used for the recommended makeup style, and virtual face image 811. The hair image 821 and information about a plurality of recommended hair shops that can perform the recommended hair style may be displayed on one screen in the display (eg, the display 160 of FIG. 1 ). For example, referring to FIG. 8 , the electronic device 101 provides a virtual face image 811, information on a plurality of recommended cosmetics usable for a recommended makeup style, a virtual hair image 821, and a recommended hair style. Information on a plurality of recommended hair shops that can be treated may be displayed on one screen of the display 160 . According to an embodiment, information on a plurality of recommended cosmetics may include a cosmetic image, a product description of the corresponding cosmetic, and a homepage link where the corresponding cosmetic can be purchased. According to an embodiment, information on a plurality of hair shops may include a hair shop image, a description of the hair shop, and a homepage link through which a reservation for the hair shop may be made. According to an embodiment, the electronic device 101 may also display a method for directing a recommended makeup styling on the virtual face image 811, and a method for directing a recommended hair style on the virtual hair image 821. method can be shown.

Meanwhile, the electronic device 101 according to an embodiment of the present invention will be described in detail with reference to FIGS. 1 to 3 below.

According to an embodiment, an electronic device includes a display and a processor, wherein the processor obtains age information of the user, a face image of the user, and an image of the user's hair from a user, and displays a facial part in the user's face image. A CNN-based first deep learning model for determining a plurality of first features and facial skin tone, determining a plurality of second features and hair color for a hair state in the user's hair image, and determining a face type. The user's face type is determined among a plurality of predetermined face types by applying the plurality of first features of the face part to the hair type, and the hair type is applied to a CNN-based second deep learning model for determining the hair type. The user's hair type is determined from among a plurality of predetermined hair types by applying the plurality of second features of the state, and based on the age information, the face type, and the facial skin tone, the hair type of the user is determined. A virtual face obtained by determining a recommended makeup style, determining a recommended hairstyle for the user based on the age information, the hair type, and the hair color, and applying an effect related to the recommended makeup style to the user's face image. An image is generated, a virtual hair image is created by applying an effect related to the recommended hairstyle to the user's hair image, and the virtual face image, information on a plurality of recommended cosmetics usable for the recommended makeup style, the virtual hair image It may be set to display a hair image and information about a plurality of recommended hair shops that can perform the recommended hair style on one screen on the display.

According to an embodiment, the processor analyzes the user's face image to determine a face shape of the user as one of the plurality of first features, and to determine the user's face shape. , [Equation 1] is used to calculate the similarity between the user's face image and pre-stored face shape images, and among the previously stored face shape images, the face shape image having the highest similarity with the user's face image The face shape may be set to determine the face shape of the user.

[Equation 1]

x _i may represent a feature vector extracted from a user's face image, and y _i may represent a feature vector extracted from a previously stored face shape image.

According to an embodiment, the processor analyzes the user's face image to determine the shape of each face part as one of the plurality of first features, and to determine the shape of each face part, An edge for specifying each face part is extracted from the user's face image, and a shape corresponding to the edge for specifying each face part among predetermined shapes for each face part is the shape of each face part. It can be set to select as

According to an embodiment, the processor may be configured to analyze the user's face image and calculate a plurality of ratio information for each face part as one of the plurality of first features.

According to an embodiment, the processor analyzes the user's hair image to determine the user's hair shape as one of the plurality of second features, and analyzes the user's hair image to determine the plurality of second features. As one of the second features, it can be set to determine the user's hair width.

According to an embodiment, the processor stores the face shape of the user, the shape of each face part, and a plurality of ratio information for each face part in a first table learned by the first deep learning model. to determine the user's face type among the plurality of predetermined face types, and apply the hair shape and hair width of the user to a second table learned by the second deep learning model, It may be set to determine the user's hair type from among the plurality of predetermined hair types.

Bus 110 may include circuitry that connects components 110-170 to each other and communicates (eg, control messages or data) between components. The processor 120 may include one or more of a central processing unit, an application processor, or a communication processor (CP). The processor 120 may, for example, execute calculations or data processing related to control and/or communication of at least one other component of the electronic device 101 .

Memory 130 may include volatile and/or non-volatile memory. The memory 130 may store, for example, commands or data related to at least one other component of the electronic device 101 . According to one embodiment, memory 130 may store software and/or programs 140 . The program 140 may include, for example, a kernel 141, middleware 143, an application programming interface (API) 145, and/or an application program (or “application”) 147, and the like. . At least part of the kernel 141, middleware 143, or API 145 may be referred to as an operating system. Kernel 141, for example, includes system resources (eg, middleware 143, API 145, or application program 147) used to execute operations or functions implemented in other programs (eg, middleware 143, API 145, or application program 147). : The bus 110, the processor 120, or the memory 130, etc.) can be controlled or managed. In addition, the kernel 141 may provide an interface capable of controlling or managing system resources by accessing individual components of the electronic device 101 from the middleware 143, API 145, or application program 147. can

The middleware 143 may perform an intermediary role so that, for example, the API 145 or the application program 147 communicates with the kernel 141 to exchange data. Also, the middleware 143 may process one or more task requests received from the application program 147 according to priority. For example, the middleware 143 may use system resources (eg, the bus 110, the processor 120, or the memory 130, etc.) of the electronic device 101 for at least one of the application programs 147. Prioritize and process the one or more work requests. The API 145 is an interface for the application 147 to control functions provided by the kernel 141 or the middleware 143, for example, at least for file control, window control, image processing, or text control. It can contain one interface or function (eg command). The input/output interface 150 transmits, for example, a command or data input from a user or other external device to other component(s) of the electronic device 101, or other components of the electronic device 101 ( s) may output commands or data received from the user or other external devices.

The display 160 may be, for example, a liquid crystal display (LCD), a light emitting diode (LED) display, an organic light emitting diode (OLED) display, a microelectromechanical system (MEMS) display, or an electronic paper display. can include The display 160 may display various types of content (eg, text, image, video, icon, and/or symbol) to the user. The display 160 may include a touch screen, and may receive, for example, a touch, gesture, proximity, or hovering input using an electronic pen or a part of the user's body. The communication interface 170 establishes communication between the electronic device 101 and an external device (eg, the first external electronic device 102 , the second external electronic device 104 , or the server 106 ). can For example, the communication interface 170 may be connected to the network 162 through wireless or wired communication to communicate with an external device (eg, the second external electronic device 104 or the server 106).

Wireless communication is, for example, LTE, LTE-A (LTE Advance), CDMA (code division multiple access), WCDMA (wideband CDMA), UMTS (universal mobile telecommunications system), WiBro (Wireless Broadband), or GSM (Global System for Mobile Communications) may include cellular communication using at least one of the like. According to one embodiment, wireless communication, for example, WiFi (wireless fidelity), Bluetooth, Bluetooth Low Energy (BLE), Zigbee, near field communication (NFC), magnetic secure transmission (Magnetic Secure Transmission), radio It may include at least one of a frequency (RF) and a body area network (BAN). According to one embodiment, wireless communication may include GNSS. The GNSS may be, for example, a Global Positioning System (GPS), a Global Navigation Satellite System (Glonass), a Beidou Navigation Satellite System (hereinafter “Beidou”) or Galileo, the European global satellite-based navigation system. Hereinafter, in this document, "GPS" may be used interchangeably with "GNSS". Wired communication may include, for example, at least one of universal serial bus (USB), high definition multimedia interface (HDMI), recommended standard 232 (RS-232), power line communication, or plain old telephone service (POTS). there is. Network 162 may include at least one of a telecommunication network, for example, a computer network (eg, LAN or WAN), the Internet, or a telephone network.

Each of the first and second external electronic devices 102 and 104 may be the same as or different from the electronic device 101 . According to an embodiment, all or part of operations executed in the electronic device 101 may be executed in one or more electronic devices (eg, the electronic devices 102 and 104, or the server 106). According to this, when the electronic device 101 needs to perform a certain function or service automatically or upon request, the electronic device 101 instead of or in addition to executing the function or service by itself, at least some functions related thereto may request another device (eg, the electronic device 102 or 104 or the server 106). The additional function may be executed and the result may be transmitted to the electronic device 101. The electronic device 101 may provide the requested function or service by processing the received result as it is or additionally. For example, cloud computing, distributed computing, or client-server computing technologies may be used.

2 is a block diagram of an electronic device 201 according to an embodiment. The electronic device 201 may include, for example, all or part of the electronic device 101 shown in FIG. 1 . The electronic device 201 includes one or more processors (eg, APs) 210, a communication module 220, a subscriber identification module 224, a memory 230, a sensor module 240, an input device 250, a display ( 260), an interface 270, an audio module 280, a camera module 291, a power management module 295, a battery 296, an indicator 297, and a motor 298. The processor 210 may control a plurality of hardware or software components connected to the processor 210 by driving, for example, an operating system or an application program, and may perform various data processing and calculations. The processor 210 may be implemented as, for example, a system on chip (SoC). According to one embodiment, the processor 210 may further include a graphic processing unit (GPU) and/or an image signal processor. The processor 210 may include at least some of the components shown in FIG. 2 (eg, the cellular module 221). The processor 210 may load and process a command or data received from at least one of the other components (eg, non-volatile memory) into a volatile memory, and store resultant data in the non-volatile memory.

It may have the same or similar configuration as the communication module 220 (eg, the communication interface 170). The communication module 220 may include, for example, a cellular module 221, a WiFi module 223, a Bluetooth module 225, a GNSS module 227, an NFC module 228 and an RF module 229. there is. The cellular module 221 may provide, for example, a voice call, a video call, a text service, or an Internet service through a communication network. According to one embodiment, the cellular module 221 may identify and authenticate the electronic device 201 within a communication network using the subscriber identity module (eg, SIM card) 224 . According to one embodiment, the cellular module 221 may perform at least some of the functions that the processor 210 may provide. According to one embodiment, the cellular module 221 may include a communication processor (CP). According to some embodiments, at least some (eg, two or more) of the cellular module 221, the WiFi module 223, the Bluetooth module 225, the GNSS module 227, or the NFC module 228 are one integrated chip (IC) or within an IC package. The RF module 229 may transmit and receive communication signals (eg, RF signals), for example. The RF module 229 may include, for example, a transceiver, a power amp module (PAM), a frequency filter, a low noise amplifier (LNA), or an antenna. According to another embodiment, at least one of the cellular module 221, the WiFi module 223, the Bluetooth module 225, the GNSS module 227, or the NFC module 228 transmits and receives an RF signal through a separate RF module. can The subscriber identification module 224 may include, for example, a card or an embedded SIM including a subscriber identification module, and may include unique identification information (eg, integrated circuit card identifier (ICCID)) or subscriber information (eg, IMSI). (international mobile subscriber identity)).

The memory 230 (eg, the memory 130) may include, for example, an internal memory 232 or an external memory 234. The built-in memory 232 may include, for example, volatile memory (eg, DRAM, SRAM, SDRAM, etc.), non-volatile memory (eg, OTPROM (one time programmable ROM), PROM, EPROM, EEPROM, mask ROM, flash ROM). , a flash memory, a hard drive, or a solid state drive (SSD).The external memory 234 may include a flash drive, for example, a compact flash (CF) or secure digital (SD). ), Micro-SD, Mini-SD, extreme digital (xD), multi-media card (MMC), or memory stick, etc. The external memory 234 is functionally compatible with the electronic device 201 through various interfaces. can be physically or physically connected.

The sensor module 240 may, for example, measure a physical quantity or detect an operating state of the electronic device 201 and convert the measured or sensed information into an electrical signal. The sensor module 240 includes, for example, a gesture sensor 240A, a gyro sensor 240B, an air pressure sensor 240C, a magnetic sensor 240D, an acceleration sensor 240E, a grip sensor 240F, and a proximity sensor ( 240G), color sensor (240H) (e.g. RGB (red, green, blue) sensor), bio sensor (240I), temperature/humidity sensor (240J), light sensor (240K), or UV (ultra violet) ) may include at least one of the sensors 240M. Additionally or alternatively, the sensor module 240 may include, for example, an e-nose sensor, an electromyography (EMG) sensor, an electroencephalogram (EEG) sensor, an electrocardiogram (ECG) sensor, It may include an IR (infrared) sensor, an iris sensor, and/or a fingerprint sensor. The sensor module 240 may further include a control circuit for controlling one or more sensors included therein. In some embodiments, the electronic device 201 further includes a processor configured to control the sensor module 240, either as part of the processor 210 or separately, so that while the processor 210 is in a sleep state, The sensor module 240 may be controlled.

The input device 250 may include, for example, a touch panel 252 , a (digital) pen sensor 254 , a key 256 , or an ultrasonic input device 258 . The touch panel 252 may use at least one of, for example, a capacitive type, a pressure-sensitive type, an infrared type, or an ultrasonic type. Also, the touch panel 252 may further include a control circuit. The touch panel 252 may further include a tactile layer to provide a tactile response to the user. The (digital) pen sensor 254 may be, for example, part of a touch panel or may include a separate recognition sheet. Keys 256 may include, for example, hardware buttons, optical keys, or keypads. The ultrasonic input device 258 may detect ultrasonic waves generated from an input tool through a microphone (eg, the microphone 288) and check data corresponding to the detected ultrasonic waves.

The display 260 (eg, the display 160) may include a panel 262, a hologram device 264, a projector 266, and/or a control circuit for controlling them. Panel 262 may be implemented to be flexible, transparent, or wearable, for example. The panel 262 may include the touch panel 252 and one or more modules. According to one embodiment, the panel 262 may include a pressure sensor (or force sensor) capable of measuring the strength of a user's touch. The pressure sensor may be implemented integrally with the touch panel 252 or may be implemented as one or more sensors separate from the touch panel 252 . The hologram device 264 may display a 3D image in the air using interference of light. The projector 266 may display an image by projecting light onto a screen. The screen may be located inside or outside the electronic device 201 , for example. Interface 270 may include, for example, HDMI 272, USB 274, optical interface 276, or D-sub (D-subminiature) 278. Interface 270 may be included in, for example, communication interface 170 shown in FIG. 1 . Additionally or alternatively, the interface 270 may include, for example, a mobile high-definition link (MHL) interface, an SD card/multi-media card (MMC) interface, or an infrared data association (IrDA) compliant interface. there is.

The audio module 280 may, for example, convert a sound and an electrical signal in both directions. At least some components of the audio module 280 may be included in the input/output interface 145 shown in FIG. 1 , for example. The audio module 280 may process sound information input or output through, for example, the speaker 282, the receiver 284, the earphone 286, or the microphone 288. The camera module 291 is, for example, a device capable of capturing still images and moving images, and according to one embodiment, one or more image sensors (eg, a front sensor or a rear sensor), a lens, and an image signal processor (ISP) , or a flash (eg, LED or xenon lamp, etc.). The power management module 295 may manage power of the electronic device 201 , for example. According to one embodiment, the power management module 295 may include a power management integrated circuit (PMIC), a charger IC, or a battery or fuel gauge. A PMIC may have a wired and/or wireless charging method. The wireless charging method includes, for example, a magnetic resonance method, a magnetic induction method, or an electromagnetic wave method, and may further include an additional circuit for wireless charging, for example, a coil loop, a resonance circuit, or a rectifier. there is. The battery gauge may measure, for example, the remaining capacity of the battery 296, voltage, current, or temperature during charging. Battery 296 may include, for example, a rechargeable battery and/or a solar cell.

The indicator 297 may indicate a specific state of the electronic device 201 or a part thereof (eg, the processor 210), for example, a booting state, a message state, or a charging state. The motor 298 may convert electrical signals into mechanical vibrations and generate vibrations or haptic effects. The electronic device 201 is, for example, a mobile TV support device capable of processing media data according to standards such as digital multimedia broadcasting (DMB), digital video broadcasting (DVB), or mediaFlo ^TM (e.g., : GPU) may be included. Each of the components described in this document may be composed of one or more components, and the name of the corresponding component may vary depending on the type of electronic device. In one embodiment, the electronic device (eg, the electronic device 201) is configured as a single entity by omitting some components, further including additional components, or combining some of the components. The functions of the previous corresponding components may be performed identically.

In one embodiment of the present invention, the electronic device 201 (or the electronic device 101) may include a housing including a front surface, a rear surface, and a side surface surrounding a space between the front surface and the rear surface. A touch screen display (eg, the display 260) is disposed within the housing and may be exposed through the front surface. A microphone 288 is disposed within the housing and may be exposed through a portion of the housing. At least one speaker 282 is disposed within the housing and may be exposed through another part of the housing. A hardware button (eg, key 256) may be located on another part of the housing or configured to display on the touchscreen display. A wireless communication circuit (eg, the communication module 220) may be located in the housing. The processor 210 (or processor 120) is located in the housing and may be electrically connected to the touch screen display, the microphone 288, the speaker 282, and the wireless communication circuit. The memory 230 (or the memory 130 ) may be located in the housing and electrically connected to the processor 210 .

In one embodiment of the present invention, the memory 230 is configured to store a first application program including a first user interface for receiving text input, and the memory 230, when executed, the The processor 210 stores instructions that cause a first operation and a second operation to be performed, and the first operation is performed through the button while the first user interface is not displayed on the touch screen display. receiving a user input of a first type, and after receiving the user input of the first type; Receives a first user utterance through the microphone 288 and provides first data for the first user utterance to an external server including an automatic speech recognition (ASR) and intelligence system. and after providing the first data, receiving at least one command for performing a task generated by the intelligent system in response to the first user utterance from the external server, the second operation, While the first user interface is displayed on the touchscreen display, the first user input is received through the button, and after receiving the first type of user input, the second user input is received through the microphone 288. Receiving a user utterance, providing second data for the second user utterance to the external server, and after providing the second data, from the server, by the automatic speech recognition from the second user utterance receive data about generated text, but not commands generated by the intelligent system; The text may be input into the first user interface.

In one embodiment of the present invention, the button may include a physical key located on the side of the housing.

In one embodiment of the present invention, the first type of user input is after pressing the button once, pressing the button twice, pressing the button three times, or pressing the button once It can be either a held press, or a two-time press and hold press on the button.

In one embodiment of the invention, the instructions may further cause the processor to display the first user interface along with a virtual keyboard. The button may not be part of the virtual keyboard.

In one embodiment of the invention, the instructions further cause the processor 210 to receive, from the external server, data for text generated by ASR from the first user utterance within the first action. can cause

In one embodiment of the present invention, the first application program may include at least one of a note application program, an e-mail application program, a web browser application program, or a calendar application program.

In one embodiment of the present invention, the first application program includes a message application, and the instructions cause the processor 210 to, if a selected time period after inputting the text exceeds, the wireless communication circuitry. It can be further caused to transmit automatically input text through.

In one embodiment of the present invention, the instructions further cause the processor 210 to perform a third operation, wherein the third operation is while displaying the first user interface on the touchscreen display. , Receives a second type of user input through the button, receives a third user speech through the microphone after receiving the second type of user input, and responds to the third user speech by the external server. After providing the third data, at least one command for performing a task generated by the intelligent system in response to the third user utterance may be received from the external server. there is.

In one embodiment of the present invention, the instructions further cause the processor 210 to perform a fourth operation, wherein the fourth operation occurs when the first user interface is not displayed on the touch screen display. On the way, the second type of user input is received through the button, and after receiving the second type of user input, a fourth user speech is received through the microphone 288, and the fourth user speech is received. Provides fourth data for the external server to the external server, and after providing the fourth data, in response to the fourth user utterance, at least one command for performing a task generated by the intelligent system is provided. After receiving from an external server, receiving a fifth user speech through the microphone, providing fifth data for the fifth user speech to the external server, and providing the fifth data, 5 In response to user speech, at least one command for performing a task generated by the intelligent system may be received from the external server.

In one embodiment of the present invention, the first type of user input and the second type of user input are different from each other, and include pressing the button once, pressing the button twice, and pressing the button three times. , Pressing and maintaining the button after pressing the button once, or pressing and maintaining the button twice and maintaining the button may be selected.

In one embodiment of the present invention, the memory 230 is further configured to store a second application program including a second user interface for receiving text input, wherein the instructions, when executed, the processor ( 210) further causes a third operation to be performed, the third operation comprising: receiving the first type of user input through the button while displaying the second user interface; After the first type of user input is received, a third user speech is received through the microphone, third data for the third user speech is provided to the external server, and the third data is provided. After receiving the data for the text generated by the ASR from the third user utterance from the external server, without receiving the command generated by the intelligent system, the text is displayed in the second user interface. and automatically transmits the entered text via the wireless communication circuitry upon entering the text and exceeding a selected period of time.

In one embodiment of the present invention, the memory 230 is configured to store a first application program including a first user interface for receiving text input, and the memory 230, when executed, the The processor 210 stores instructions that cause a first operation and a second operation to be performed, the first operation receives a first type of user input through the button, and the first type of user input After receiving , the first user utterance is received through the microphone 288, and an external server including an automatic speech recognition (ASR) and intelligence system is configured to receive the first user utterance. Provide first data for, and, after providing the first data, receive at least one command for performing a task generated by the intelligent system in response to the first user utterance from the external server. and the second operation receives a second type of user input through the button and, after receiving the second type of user input, receives a second user utterance through the microphone 288; After providing the second data for the second user utterance to the external server, and receiving data for text generated by ASR from the second user utterance from the server after providing the second data, , the command generated by the intelligent system is not received, and the text can be input to the first user interface.

In one embodiment of the present invention, the instructions may further cause the processor 210 to display the first user interface along with a virtual keyboard, and the button may not be part of the virtual keyboard.

In one embodiment of the invention, the instructions further cause the processor 210 to receive data for text generated by the ASR from the first user utterance within the first action from the external server. can do.

In one embodiment of the present invention, the first application program may include at least one of a note application program, an e-mail application program, a web browser application program, or a calendar application program.

In one embodiment of the present invention, the first application program includes a message application, and the instructions cause the processor 210 to, if a selected time period after inputting the text exceeds, the wireless communication circuitry. It can be further caused to transmit automatically input text through.

In one embodiment of the invention, the instructions may further cause the processor 210 to perform the first operation independently of display on the display of the first user interface.

In one embodiment of the present invention, the instructions include performing the second operation when the processor 210 is at least one of the electronic device being locked or the touch screen display being turned off. can cause more

In one embodiment of the present invention, the instructions may further cause the processor 210 to perform the second operation while displaying the first user interface on the touch screen display.

In one embodiment of the present invention, the memory 230, when executed, allows the processor 210 to receive user utterances through the microphone 288, to perform automatic speech recognition (ASR) or An external server that performs at least one of natural language understanding (NLU), and performs the ASR on the user utterance data together with the user utterance data to obtain the natural language understanding for text obtained. transmits information associated with whether or not to perform the natural language understanding, and if the information indicates not to perform the natural language understanding, receive the text for data on the user's utterance from the external server; If indicated, an instruction causing the external server to receive a command obtained as a result of performing the natural language understanding of the text may be stored.

3 is a block diagram of program modules according to one embodiment. According to one embodiment, the program module 310 (eg, the program 140) is an operating system that controls resources related to an electronic device (eg, the electronic device 101) and/or various applications running on the operating system (eg, the electronic device 101). Example: application program 147). The operating system may include, for example, Android ^TM , iOS ^TM , Windows ^TM , Symbian ^TM , Tizen ^TM , or Bada ^TM . Referring to FIG. 3, the program module 310 includes a kernel 320 (eg kernel 141), middleware 330 (eg middleware 143), (API 360 (eg API 145) ), and/or an application 370 (eg, the application program 147). At least a part of the program module 310 is preloaded on an electronic device, or an external electronic device (eg, an electronic device ( 102, 104), server 106, etc.).

The kernel 320 may include, for example, a system resource manager 321 and/or a device driver 323 . The system resource manager 321 may perform control, allocation, or recovery of system resources. According to one embodiment, the system resource manager 321 may include a process management unit, a memory management unit, or a file system management unit. The device driver 323 may include, for example, a display driver, a camera driver, a Bluetooth driver, a shared memory driver, a USB driver, a keypad driver, a WiFi driver, an audio driver, or an inter-process communication (IPC) driver. . The middleware 330, for example, provides functions commonly required by the application 370 or provides various functions through the API 360 so that the application 370 can use limited system resources inside the electronic device. It can be provided as an application 370 . According to one embodiment, the middleware 330 includes a runtime library 335, an application manager 341, a window manager 342, a multimedia manager 343, a resource manager 344, a power manager 345, a database manager ( 346), a package manager 347, a connectivity manager 348, a notification manager 349, a location manager 350, a graphic manager 351, or a security manager 352.

The runtime library 335 may include, for example, a library module used by a compiler to add new functions through a programming language while the application 370 is being executed. The runtime library 335 may perform input/output management, memory management, or arithmetic function processing. The application manager 341 may manage the life cycle of the application 370 , for example. The window manager 342 may manage GUI resources used in the screen. The multimedia manager 343 may determine a format required for reproducing media files, and encode or decode the media files using a codec suitable for the format. The resource manager 344 may manage a source code of the application 370 or a memory space. The power manager 345 may manage, for example, battery capacity or power, and provide power information necessary for the operation of the electronic device. According to one embodiment, the power manager 345 may interoperate with a basic input/output system (BIOS). The database manager 346 may create, search, or change a database to be used in the application 370 , for example. The package manager 347 may manage installation or update of applications distributed in the form of package files.

The connectivity manager 348 may manage wireless connections, for example. The notification manager 349 may provide a user with an event such as an arrival message, an appointment, or proximity notification. The location manager 350 may manage, for example, location information of an electronic device. The graphic manager 351 may manage, for example, graphic effects to be provided to users or user interfaces related thereto. Security manager 352 may provide system security or user authentication, for example. According to one embodiment, the middleware 330 may include a telephony manager for managing voice or video call functions of an electronic device or a middleware module capable of forming a combination of functions of the aforementioned components. . According to one embodiment, the middleware 330 may provide modules specialized for each type of operating system. The middleware 330 may dynamically delete some existing components or add new components. The API 360 is, for example, a set of API programming functions, and may be provided in different configurations depending on the operating system. For example, in the case of Android or iOS, one API set can be provided for each platform, and in the case of Tizen, two or more API sets can be provided for each platform.

The application 370 includes, for example, a home 371, a dialer 372, an SMS/MMS 373, an instant message (IM) 374, a browser 375, a camera 376, and an alarm 377. , Contacts (378), Voice Dial (379), Email (380), Calendar (381), Media Player (382), Album (383), Watch (384), Health Care (e.g. exercise or blood sugar measurement) , or environmental information (eg, air pressure, humidity, or temperature information) providing applications. According to one embodiment, the application 370 may include an information exchange application capable of supporting information exchange between an electronic device and an external electronic device. The information exchange application may include, for example, a notification relay application for delivering specific information to an external electronic device or a device management application for managing an external electronic device. For example, a notification delivery application may transfer notification information generated by another application of an electronic device to an external electronic device or may receive notification information from an external electronic device and provide the notification information to a user. The device management application is, for example, a function of an external electronic device communicating with the electronic device (eg, turn-on/turn-off of the external electronic device itself (or some component parts) or display brightness (or resolution)). adjustment), or an application operating in an external electronic device may be installed, deleted, or updated. According to an embodiment, the application 370 may include an application designated according to the properties of an external electronic device (eg, a health management application of a mobile medical device). According to one embodiment, the application 370 may include an application received from an external electronic device. At least a portion of the program module 310 may be implemented (eg, executed) in software, firmware, hardware (eg, the processor 210), or a combination of at least two of them, and a module for performing one or more functions; It can include a program, routine, set of instructions, or process.

The term "module" used in this document includes a unit composed of hardware, software, or firmware, and may be used interchangeably with terms such as logic, logic block, component, or circuit, for example. A “module” may be an integrally constructed component or a minimal unit or part thereof that performs one or more functions. A "module" may be implemented mechanically or electronically, for example, a known or future developed application-specific integrated circuit (ASIC) chip, field-programmable gate arrays (FPGAs), or A programmable logic device may be included. At least a part of an apparatus (eg, modules or functions thereof) or a method (eg, operations) according to an embodiment is a program module form and instructions stored in a computer-readable storage medium (eg, the memory 130). can be implemented as When the command is executed by a processor (eg, the processor 120), the processor may perform a function corresponding to the command. Computer-readable recording media include hard disks, floppy disks, magnetic media (e.g. magnetic tape), optical recording media (e.g. CD-ROM, DVD, magneto-optical media (e.g. floptical disks), built-in memory, etc.) A command may include code generated by a compiler or code executable by an interpreter A module or program module according to an embodiment may include at least one or more of the above-described components, or , some may be omitted, or may further include other elements.According to an embodiment, operations performed by modules, program modules, or other elements may be executed sequentially, in parallel, iteratively, or heuristically, or at least Some actions may be performed in a different order, omitted, or other actions may be added.

And the embodiments disclosed in this document are presented for explanation and understanding of the disclosed technical content, and do not limit the scope of the present disclosure. Therefore, the scope of the present disclosure should be construed to include all changes or various other embodiments based on the technical spirit of the present disclosure.

Claims (10)

In electronic devices,
display; and a processor; including,
The processor:
Acquiring the user's age information, the user's face image, and the user's hair image from the user;
determining a plurality of first features and a facial skin tone of a facial part in the user's face image, and determining a plurality of second features and hair color of a hair state in the user's hair image;
The user's face type is determined among a plurality of predetermined face types by applying the plurality of first features of the face part to a CNN-based first deep learning model for determining the face type, and the hair type is determined. determining the user's hair type from among a plurality of predetermined hair types by applying the plurality of second features of the hair state to a CNN-based second deep learning model for determining;
A recommended makeup style for the user is determined based on the age information, the face type, and the facial skin tone, and a recommended hairstyle for the user is determined based on the age information, the hair type, and the hair color. to determine,
generating a virtual face image by applying the effect of the recommended makeup style to the user's face image, and generating a virtual hair image by applying the effect of the recommended hairstyle to the user's hair image;
The virtual face image, information about a plurality of recommended cosmetics that can be used for the recommended makeup style, the virtual hair image, and information about a plurality of recommended hair shops that can treat the recommended hair style are displayed on one screen on the display. Displayed within, an electronic device.
According to claim 1,
the processor,
Analyzing the user face image to determine a face shape of the user as one of the plurality of first features;
In order to determine the user's face shape, the electronic device calculates a similarity between the user's face image and pre-stored face shape images using [Equation 1].
[Equation 1]

x _i denotes a feature vector extracted from a user's face image, and y _i denotes a feature vector extracted from a pre-stored face shape image.
According to claim 2,
The processor determines, as the user's face shape, a face shape of a face shape image having the highest similarity to the user's face image among the pre-stored face shape images.
According to claim 3,
the processor,
The electronic device of analyzing the user's face image and determining a shape of each face part as one of the plurality of first features.
According to claim 4,
the processor,
The electronic device of extracting an edge specifying each face part from the user's face image in order to determine the shape of each face part.
According to claim 5,
the processor,
The electronic device of claim 1 , wherein a shape corresponding to an edge specifying each face part is selected as the shape of each face part from among predetermined shapes for each face part.
According to claim 6,
The electronic device of claim 1 , wherein the processor analyzes the user face image and calculates a plurality of ratio information for each face part as one of the plurality of first features.
According to claim 7,
The processor:
Analyzing the user's hair image to determine a hair shape of the user as one of the plurality of second features;
and analyzing the user's hair image to determine, as one of the plurality of second characteristics, a width of the user's hair.
According to claim 8,
the processor,
The face shape of the user, the shape of each face part, and the plurality of ratio information for each face part are applied to the first table learned by the first deep learning model, and the plurality of predetermined face types are applied. An electronic device that determines the user's face type from among them.
According to claim 9,
the processor,
The electronic device is set to determine the user's hair type from among the plurality of predetermined hair types by applying the hair shape and the hair width of the user to a second table learned by the second deep learning model.

Images