Face information processing method and device, terminal and storage medium

Abstract

The invention relates to a face information processing method and device, a terminal and a storage medium. The face information processing method comprises the following steps: acquiring face information of a user; extracting face key features according to the face information of the user; according to the face key features, corresponding user feature information is output, and the user feature information at least comprises character features; and outputting recommendation information corresponding to the user feature information according to the user feature information. According to the scheme provided by the invention, more comprehensive feature analysis processing can be performed on the face data, character feature analysis and associated recommendation services are provided for the user, and the use experience of the user is improved.

Description

Technical Field

[0001] This application relates to the field of facial analysis technology, and in particular to a facial information processing method, device, terminal and storage medium. Background Technology

[0002] With the development of computer technology, image analysis technology and mobile Internet technology, facial analysis and processing technology has developed very rapidly and has been gradually applied in various industries.

[0003] In related technologies, various facial feature analysis techniques can be used to analyze face type and estimate age. However, these technologies only perform simple analysis of facial features, resulting in overly simplistic information processing that fails to provide users with more comprehensive analytical services, thus impacting the user experience. Summary of the Invention

[0004] To address or partially address the problems existing in related technologies, this application provides a face information processing method, apparatus, terminal, and storage medium, which can perform more comprehensive feature analysis and processing on face data, provide users with personality feature analysis and related recommendation services, and improve user experience.

[0005] The first aspect of this application provides a method for processing facial information, the method comprising:

[0006] Obtain user facial information;

[0007] Based on the user's facial information, extract key facial features;

[0008] Based on the key facial features, output the corresponding user feature information, which includes at least personality features.

[0009] Based on the user characteristic information, output recommendation information corresponding to the user characteristic information.

[0010] In one embodiment, the user feature information further includes facial state information, and the step of outputting recommendation information corresponding to the user feature information includes:

[0011] Based on the facial state information and personality traits, recommended products corresponding to the user's characteristic information are output.

[0012] In one embodiment, outputting corresponding user feature information based on the key facial features includes:

[0013] Based on the key facial features and the mapping relationship between the key facial features and facial state information, output the facial state information corresponding to the key facial features;

[0014] Based on the facial state information and the mapping relationship between the facial state information and personality traits, the personality traits corresponding to the facial state information are output.

[0015] In one embodiment, the key facial features include facial features and facial expression features;

[0016] Based on the key facial features and the mapping relationship between the key facial features and facial state information, the facial state information corresponding to the key facial features is output, including:

[0017] Based on the facial features and facial expression features, and the mapping relationship between the facial features and facial expression features and facial state information, the corresponding facial state information is output.

[0018] In one embodiment, the facial state information includes at least one of age perception, distance perception, and intelligence perception;

[0019] The step of outputting the personality traits corresponding to the facial state information based on the facial state information and the mapping relationship between the facial state information and personality traits includes:

[0020] Obtain the feature vectors of the sense of age, sense of distance, and sense of wisdom;

[0021] Based on the feature vectors of the sense of age, sense of distance, and sense of wisdom, and the mapping relationship between the feature vectors of the sense of age, sense of distance, and sense of wisdom and the preset personality assessment dimensions, the corresponding personality characteristics are output.

[0022] In one embodiment, the facial state information includes at least one of age perception, distance perception, and intelligence perception;

[0023] The step of outputting the personality traits corresponding to the facial state information based on the facial state information and the mapping relationship between the facial state information and personality traits includes:

[0024] Obtain the feature vectors of the sense of age, sense of distance, and sense of wisdom;

[0025] Based on the feature vectors of perceived age, perceived distance, and perceived intelligence, facial style features are determined, wherein the facial style features reflect personality traits;

[0026] Based on the mapping relationship between the facial style features and the preset personality assessment dimensions, the corresponding personality features are output.

[0027] In one embodiment, the method further includes:

[0028] Obtain the user's response based on the recommendation information;

[0029] Based on the user's response, an execution result corresponding to the response is generated.

[0030] In one embodiment, when outputting the recommendation information, the recommendation information is output by combining the user's preference data and / or the user's historical recommendation data.

[0031] In one embodiment, the recommendation information is displayed in the form of charts and / or text.

[0032] A second aspect of this application provides a method for processing facial information, the method comprising:

[0033] Obtain user facial information;

[0034] Based on the user's facial information, extract key facial features;

[0035] Based on the key facial features, output the corresponding personality traits.

[0036] In one embodiment, the step of outputting corresponding personality traits based on the key facial features includes:

[0037] Based on the key facial features and the mapping relationship between the key facial features and facial state information, output the facial state information corresponding to the key facial features;

[0038] Based on the facial state information and the mapping relationship between the facial state information and personality traits, the personality traits corresponding to the facial state information are output.

[0039] In one embodiment, the facial state information includes at least one of age perception, distance perception, and intelligence perception;

[0040] The step of outputting the personality traits corresponding to the facial state information based on the facial state information and the mapping relationship between the facial state information and personality traits includes:

[0041] Obtain the feature vectors of the sense of age, sense of distance, and sense of wisdom;

[0042] Based on the feature vectors of the sense of age, sense of distance, and sense of wisdom, and the mapping relationship between the feature vectors of the sense of age, sense of distance, and sense of wisdom and the preset personality assessment dimensions, the corresponding personality characteristics are output.

[0043] In one embodiment, the facial state information includes at least one of age perception, distance perception, and intelligence perception;

[0044] The step of outputting the personality traits corresponding to the facial state information based on the facial state information and the mapping relationship between the facial state information and personality traits includes:

[0045] Obtain the feature vectors of the sense of age, sense of distance, and sense of wisdom;

[0046] Based on the feature vectors of perceived age, perceived distance, and perceived intelligence, facial style features are determined, wherein the facial style features reflect personality traits;

[0047] Based on the mapping relationship between the facial style features and the preset personality assessment dimensions, the corresponding personality features are output.

[0048] In one embodiment, the personality traits are displayed in the form of charts and / or text.

[0049] A third aspect of this application provides a face information processing device, the device comprising:

[0050] The information acquisition module is used to acquire the user's facial information;

[0051] The feature extraction module is used to extract key facial features based on the user's facial information;

[0052] The analysis and processing module is used to output corresponding user feature information based on the key facial features, wherein the user feature information includes at least personality features.

[0053] The information recommendation module is used to output recommended information corresponding to the user feature information based on the user feature information.

[0054] A fourth aspect of this application provides a face information processing apparatus, the apparatus comprising:

[0055] The information acquisition module is used to acquire the user's facial information;

[0056] The feature extraction module is used to extract key facial features based on the user's facial information;

[0057] The personality analysis module is used to output corresponding personality traits based on the key facial features.

[0058] The fifth aspect of this application provides a terminal, including:

[0059] Processor; and

[0060] A memory that stores executable code, which, when executed by the processor, causes the processor to perform the method described above.

[0061] A sixth aspect of this application provides a computer-readable storage medium having executable code stored thereon, which, when executed by a processor of an electronic device, causes the processor to perform the method described above.

[0062] The technical solution provided in this application may include the following beneficial effects:

[0063] The technical solution of this application involves extracting key facial features from the user's facial information, then outputting corresponding user feature information based on these key facial features. This user feature information includes at least personality traits. Finally, based on the user feature information, recommendation information corresponding to the user feature information is output. Through this process, this application can analyze a user's personality traits and output recommendation information corresponding to those traits. This enables more comprehensive feature analysis of facial data, providing users with personality trait analysis and related recommendation services, thereby improving the user experience.

[0064] The technical solution of this application extracts key facial features from the user's facial information and then outputs corresponding personality traits based on these features. Through the above processing, this application can analyze personality traits for the user, thereby enabling more comprehensive feature analysis of facial data, providing users with personality trait analysis, and improving the user experience.

[0065] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0066] The above and other objects, features and advantages of this application will become more apparent from the more detailed description of exemplary embodiments thereof in conjunction with the accompanying drawings, wherein the same reference numerals generally represent the same components in the exemplary embodiments thereof.

[0067] Figure 1 This is a schematic diagram of the first process of a face information processing method shown in an embodiment of this application;

[0068] Figure 2 This is a schematic diagram of the second process of a face information processing method shown in an embodiment of this application;

[0069] Figure 3 This is a schematic diagram of the third process of a face information processing method shown in an embodiment of this application;

[0070] Figure 4 This is a schematic diagram of the fourth process of a face information processing method shown in an embodiment of this application;

[0071] Figure 5 This is a first structural schematic diagram of a face information processing device shown in an embodiment of this application;

[0072] Figure 6 This is a second structural schematic diagram of the face information processing device shown in the embodiments of this application;

[0073] Figure 7 This is a schematic diagram of the third structure of the face information processing device shown in the embodiments of this application;

[0074] Figure 8 This is a fourth structural schematic diagram of the face information processing device shown in the embodiments of this application;

[0075] Figure 9 This is a schematic diagram of the terminal structure shown in an embodiment of this application. Detailed Implementation

[0076] Embodiments of this application will now be described in more detail with reference to the accompanying drawings. While embodiments of this application are shown in the drawings, it should be understood that this application may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to make this application more thorough and complete, and to fully convey the scope of this application to those skilled in the art.

[0077] The terminology used in this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The singular forms “a,” “the,” and “the” used in this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0078] It should be understood that although the terms "first," "second," "third," etc., may be used in this application to describe various information, this information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, without departing from the scope of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0079] The existing technologies simply analyze facial features, resulting in overly simplistic information processing that fails to provide users with more comprehensive analysis and processing services, thus impacting the user experience.

[0080] To address the aforementioned issues, this application provides a facial information processing method that enables more comprehensive feature analysis of facial data, providing users with personality trait analysis and related recommendation services, thereby improving the user experience.

[0081] The technical solutions of the embodiments of this application are described in detail below with reference to the accompanying drawings.

[0082] Figure 1This is a schematic diagram of the first process of a face information processing method shown in an embodiment of this application.

[0083] See Figure 1 The method includes:

[0084] S101. Obtain user's facial information.

[0085] Among these methods, user facial information can be obtained by collecting facial images.

[0086] S102. Extract key facial features based on the user's facial information.

[0087] Among these features, key facial features can be extracted based on a pre-trained face detection model and user facial information.

[0088] Among them, key facial features may include facial features and / or facial expression features.

[0089] Taking key facial features as an example, this involves extracting key feature points from the facial region, such as the features of the eyes, nose, and mouth. Facial features can include wrinkles, skin condition, and other characteristics. Facial expression features include, for example, smiling and frowning.

[0090] S103. Based on the key facial features, output the corresponding user feature information, which shall include at least personality features.

[0091] In this application, the user feature information can be obtained based on a pre-established user feature information prediction model, that is, by inputting key facial features into the prediction model for processing, the corresponding user feature information is obtained; or, the user feature information can also be based on a preset correspondence between key facial features and user feature information, and by matching the user feature information corresponding to the user's facial information through the preset correspondence between key facial features and user feature information.

[0092] Optionally, in this application, the user characteristic information may also include facial status information.

[0093] To obtain user personality traits more accurately, this application provides a feasible solution for step S103, specifically including:

[0094] Based on the key facial features and the mapping relationship between the key facial features and facial state information, output the facial state information corresponding to the key facial features;

[0095] Based on the facial state information and the mapping relationship between facial state information and personality traits, the personality traits corresponding to the facial state information are output.

[0096] Among them, key facial features can include facial facial features and facial expression features; when outputting facial state information corresponding to key facial features based on key facial features and the mapping relationship between key facial features and facial state information, the corresponding facial state information can be output based on facial facial features and facial expression features, and the mapping relationship between facial facial features and facial expression features and facial state information.

[0097] The facial status information in this application may include at least one of the following: perceived age, perceived distance, and perceived intelligence.

[0098] In one embodiment, when outputting personality traits corresponding to facial state information based on facial state information and the mapping relationship between facial state information and personality traits, feature vectors of sense of age, sense of distance, and sense of wisdom can be obtained; and corresponding personality traits can be output based on the feature vectors of sense of age, sense of distance, and sense of wisdom, and the mapping relationship between the feature vectors of sense of age, sense of distance, and sense of wisdom and preset personality assessment dimensions.

[0099] In another embodiment, when outputting personality traits corresponding to facial state information based on facial state information and the mapping relationship between facial state information and personality traits, it may also involve obtaining feature vectors of age perception, distance perception, and intelligence perception; determining facial style features based on the feature vectors of age perception, distance perception, and intelligence perception, wherein facial style features reflect personality traits; and outputting corresponding personality traits based on the mapping relationship between facial style features and preset personality assessment dimensions.

[0100] S104. Based on user characteristic information, output recommendation information corresponding to the user characteristic information.

[0101] This application extracts key facial features from a user's facial information, and then outputs corresponding user feature information based on these features. This user feature information includes at least personality traits. Next, based on the user feature information, it outputs recommendation information corresponding to those traits. Through this process, this application can analyze a user's personality traits and output recommendation information corresponding to those traits. This enables more comprehensive feature analysis of facial data, providing users with personality trait analysis and related recommendation services, thereby improving the user experience.

[0102] In order to provide more accurate recommendation information to users, in conjunction with the above implementation method, a feasible solution is provided for step S104. Specifically, the user feature information also includes facial state information. This step may include: outputting recommended products corresponding to the user feature information based on the facial state information and personality characteristics.

[0103] Furthermore, when outputting recommendation information, user preference data and / or user historical recommendation data can also be combined to output recommendation information.

[0104] The recommended information is displayed in the form of charts and / or text.

[0105] Figure 2 This is a schematic diagram of the second process of a face information processing method shown in an embodiment of this application.

[0106] See Figure 2 The method includes:

[0107] S201. Obtain user's facial information.

[0108] In this step, facial images are captured to obtain the user's facial information. For example, a camera can be used to capture an image of the user's face. At this point, it's important to ensure image clarity and appropriate lighting conditions. Additionally, camera parameters can be automatically adjusted to adapt to different lighting conditions.

[0109] After acquiring the face image, further image preprocessing can be performed, such as grayscale conversion and noise reduction. Grayscale conversion reduces computational complexity. Gaussian blur filters can be used to remove noise, and edge detection algorithms can enhance boundary information.

[0110] S202. Extract key facial features based on the pre-trained face detection model and user facial information.

[0111] This application can use a pre-trained face detection model to identify the facial region of a face image and extract key facial features from the facial region.

[0112] One approach is to train a face detection model using deep learning methods, specifically a CNN (Convolutional Neural Network)-based model. A Convolutional Neural Network is a type of feedforward neural network that incorporates convolutional computations and has a deep structure; it is one of the representative algorithms of deep learning. Convolutional Neural Networks possess representation learning capabilities, enabling them to perform translation-invariant classification of input information according to their hierarchical structure.

[0113] One approach is to use the single-stage detection algorithm of SSD (Single Shot MultiBox Detector) to identify and detect face images.

[0114] In the pre-training process of the face detection model, data preparation can begin, such as collecting and labeling face image datasets. Then, model construction can be performed, where the base network VGG16 can be selected as the feature extractor, and additional convolutional layers can be added to predict bounding boxes at different scales. A loss function can also be designed. During training, the dataset can be divided into training, validation, and test sets, and then trained using the stochastic gradient descent Adam optimizer. Simultaneously, the loss and accuracy are monitored during training, and hyperparameters such as the learning rate are adjusted. Further evaluation and tuning are performed, including evaluating model performance on the validation set and adjusting the model structure and training strategy based on the evaluation results. Finally, testing and deployment are conducted, including a final evaluation on the test set, and then deploying the model to real-world applications. VGG16 is a deep learning-based convolutional neural network model, whose core structure consists of a series of convolutional and pooling layers. The main function of the Adam optimizer is to update the neural network parameters based on gradient information, thereby minimizing the loss function.

[0115] It should be noted that the detected facial images can be standardized in size to ensure that all facial images are the same size.

[0116] After identifying the facial region of a face image using a pre-trained face detection model, feature extraction processing is performed, including extracting key facial features from the facial region.

[0117] Among them, key facial features may include facial features and / or facial expression features.

[0118] Taking facial key features as an example, key feature points are extracted from the facial region, such as facial features like the eyes, nose, and mouth. Facial feature features can include wrinkles, skin condition, and other characteristics. Then, facial feature vectors of the key feature points are calculated, including but not limited to facial contours, wrinkles, and blemishes.

[0119] S203. Based on the key facial features and the mapping relationship between the key facial features and facial state information, output the facial state information corresponding to the key facial features.

[0120] Key facial features can include facial features and / or facial expression features. By further analyzing the changing trends of facial expressions, such as smiling and frowning, corresponding facial state features can be matched more accurately, which is more conducive to assisting subsequent personality analysis. Therefore, this application analyzes facial expressions, eye contact, and smiling frequency to better assist in subsequent assessment of personality tendencies.

[0121] Among them, facial status information includes at least one of age perception, distance perception, and intelligence perception.

[0122] Among them, sense of age, sense of distance, and sense of wisdom can be described by feature vectors, which can be in the form of feature values ​​or other forms.

[0123] The definitions and descriptions of sense of age, sense of distance, and sense of wisdom in this application are provided below:

[0124] Age perception (A): Reflects the perceived age of the user, whether they appear young or mature.

[0125] Age perception can be categorized in the following ways, but is not limited to these:

[0126] A1 (Negative): If the characteristic value of perceived age is less than the preset threshold, and the facial features show more wrinkles and loose skin, it indicates that the perceived age is negative, meaning the person appears older than their actual age.

[0127] A2 (Neutral): If the characteristic value of perceived age is equal to the preset threshold, and the facial features show moderate wrinkles and skin condition, then the perceived age is neutral, that is, close to the actual age.

[0128] A3 (Positive): If the characteristic value of perceived age is greater than the preset threshold, and the facial features show fewer wrinkles and firm skin, then the perceived age is positive, meaning the person appears younger than their actual age.

[0129] Sense of distance (B): Reflects whether a user gives off a feeling of closeness or distance.

[0130] The sense of distance can be categorized in the following ways, but is not limited to these:

[0131] B1 (Negative): If the feature value of the sense of distance is less than the preset threshold, and the facial expression shows more frowning and closed-off expressions, it indicates that the sense of distance is biased towards the negative, that is, it appears more distant.

[0132] B2 (Neutral): If the characteristic value of the sense of distance is equal to the preset threshold, and the facial expression shows a neutral expression, then the sense of distance is neutral, that is, neither close nor distant.

[0133] B3 (Positive): If the feature value of the sense of distance is greater than the preset threshold, and the facial expression shows more smiling and open expressions, it indicates that the sense of distance is biased towards the positive, that is, it appears more intimate.

[0134] Intelligence (C): Reflects whether the user gives the impression of being intelligent or thoughtful.

[0135] The sense of intelligence can be categorized in the following ways, but is not limited to these:

[0136] C1 (Negative): If the characteristic value of intelligence is less than the preset threshold, and the facial expression shows more confusion and bewilderment, it indicates that the intelligence is biased towards the negative, that is, it appears not smart enough.

[0137] C2 (Neutral): If the characteristic value of intelligence is equal to the preset threshold, and the facial expression displays a neutral expression, then the intelligence is neutral, meaning that the person neither appears particularly intelligent nor not intelligent enough.

[0138] C3 (Positive): If the characteristic value of intelligence is greater than the preset threshold, and the facial expression shows more confidence and focus, it indicates that the intelligence is biased towards the positive, that is, the person appears to be quite intelligent.

[0139] It should be noted that the above degree descriptive terms (such as more, less, neutral, etc.) can be determined based on the feature values ​​returned by the deep learning algorithm service.

[0140] The service interface of the deep algorithm service returns the item_feature field, which represents the feature of a certain part of the face and includes a score field. The score value is obtained according to different parts, and the corresponding classification is determined by comparing the score value with a preset threshold.

[0141] For example, if the score value is less than the preset threshold, such as less than 32.3985, it is defined as A1, which means that the facial features show more wrinkles and loose skin.

[0142] S204. Based on the facial state information and the mapping relationship between facial state information and personality traits, output the personality traits corresponding to the facial state information.

[0143] This step involves further data analysis, including personality analysis based on facial status information.

[0144] This step can obtain feature vectors of perceived age, perceived distance, and perceived wisdom; based on the feature vectors of perceived age, perceived distance, and perceived wisdom, and the mapping relationship between these feature vectors and preset personality assessment dimensions, the corresponding personality traits are output.

[0145] The preset personality assessment dimensions can include four dimensions, such as attention direction, cognitive style, judgment style, and lifestyle.

[0146] This application uses MBTI personality type analysis as an example, but is not limited to it.

[0147] Among them, based on the combination of sense of age (A), sense of distance (B), and sense of wisdom (C), the corresponding personality traits can be derived, such as the corresponding MBTI personality type.

[0148] MBTI comprises four dimensions and 16 personality types. The four dimensions mainly include attention direction, cognitive style, judgment style, and lifestyle.

[0149]

[0150] In other words, based on four dimensions, MBTI personality types include introversion (I) and extroversion (E), sensing (S) and intuition (N), thinking (T) and feeling (F), judging (J) and perceiving (P). Based on these types, sixteen personality traits can be formed, which can be called the sixteen personality types.

[0151] This application can output corresponding personality traits based on the combination of sense of age, sense of distance, and sense of intelligence, and the mapping relationship between the combination and the four personality assessment dimensions of MBTI.

[0152] Among them, feature vectors of sense of age, sense of distance, and sense of wisdom can be obtained, forming a combination of feature vectors. Based on the combination of feature vectors of sense of age, sense of distance, and sense of wisdom, and the mapping relationship between the combination of feature vectors of sense of age, sense of distance, and sense of wisdom and the four personality assessment dimensions of MBTI, the corresponding personality characteristics are output.

[0153] This process involves acquiring feature vectors representing perceived age, distance, and intelligence. Based on these feature vectors, facial style features are determined, reflecting personality traits. Finally, based on the mapping relationship between facial style features and preset personality assessment dimensions, corresponding personality traits are output. For example, if a user exhibits many smiles and open expressions (B3), appears younger (A3), and seems intelligent (C3), the final result is A3B3C3.

[0154] Based on the combination of A3B3C3, the generated facial style features are "dignified and composed, with a straight and upright posture, a firm and confident gaze, giving a reliable and mature impression. The facial features are solid, with high cheekbones and a defined chin, all reflecting your determination and confidence."

[0155] Based on the mapping relationship between facial style features and the four personality assessment dimensions of MBTI, the corresponding ESTJ (Extroverted, Sensing, Reasoning, Judging) personality type is matched.

[0156] It should be noted that the mapping relationship can be represented by a matching table.

[0157] It should also be noted that for the combination A3B3C3, if it is a woman, the matching face shape is a cool and sophisticated face; if it is a man, the matching face shape is a cool and handsome face. The corresponding evaluation keywords are "dignified and steady, upright, with a firm and confident gaze, giving people a reliable and mature feeling. Solid facial features, high cheekbones and a well-defined chin, all reflect your determination and confidence."

[0158] For example, in the combination A3B1C3, B1 represents a negative sense of distance, indicating that the user is very distant. The woman's face shape indicates a high level of maturity, conveying a mature and substantial sense of distance. The man's face shape also indicates a high level of maturity, with a rounded overall outline, but he exudes a sense of distance and wealth. The corresponding evaluation keywords are "reserved, confident, deep eyes, well-defined features, fair skin, high intelligence, and independence".

[0159] It should also be noted that this application may not generate facial style features; instead, it may directly output the corresponding personality features based on the mapping relationship between the combination of feature vectors of sense of age, sense of distance, and sense of intelligence and the four personality assessment dimensions of MBTI.

[0160] For example, if a user displays a lot of smiling, open expressions (B3), appears younger (A3), and appears intelligent (C3), the final combination is A3B3C3.

[0161] Based on the mapping relationship between the combination of A3B3C3 and the four personality assessment dimensions of MBTI, the corresponding ESTJ (Extroverted, Sensing, Reasoning, Judging) personality type is matched.

[0162] When outputting personality traits, a detailed MBTI personality test report can be generated. This report can include scores and suggestions for various indicators, such as scores for Introversion (I) and Extroversion (E), Sensing (S) and Intuition (N), Thinking (T) and Feeling (F), Judging (J) and Perceiving (P), etc.

[0163] Among them, the interpretation of personality types can describe the typical characteristics of each personality type to help users understand their own personality type.

[0164] The interpretation of sense of age, sense of distance, and sense of wisdom can be based on the scores of the three dimensions: sense of age (A), sense of distance (B), and sense of wisdom (C), to help users understand their own sense of age, sense of distance, and sense of wisdom.

[0165] Finally, comprehensive suggestions can be provided to users, including combining scores from three dimensions: personality type and sense of age (A), sense of distance (B), and sense of wisdom (C), to help users better understand themselves and improve their lives.

[0166] In addition, when outputting reports, they can be presented to users in the form of charts and / or text.

[0167] This application can also directly output personality features corresponding to facial key features based on facial key features and the mapping relationship between facial key features and personality features.

[0168] Key facial features can include facial features and / or facial expression features. Facial features can include wrinkles, skin condition, etc. Facial expression features include, for example, smiling, frowning, etc.

[0169] For example, if the detected facial features show fewer wrinkles, tight skin, and facial expressions that show more smiles and openness, as well as more confident and focused expressions, then based on the mapping relationship between these key facial features and the four personality assessment dimensions of MBTI, the corresponding ESTJ (Extroverted, Sensing, Reasoning) personality type can be matched.

[0170] It should be noted that the mapping relationship can be represented by a matching table.

[0171] S205. Based on facial status information and personality traits, output recommended products corresponding to the user's characteristic information.

[0172] This application can make recommendations based on a combination of facial features and personality traits. For example, if a user is categorized as an ESTJ personality type and has a score of A3B3C3, it means they appear young, approachable, and intelligent. Therefore, suitable products can be recommended based on the following characteristics:

[0173] Nose: For nasal problems, we recommend nose strips and nose masks. If the nasal features show that the nostrils are wide, we can recommend products that can reduce the width of the nostrils.

[0174] Eyes: If the eye area shows obvious eye bags, products for removing eye bags can be recommended.

[0175] Mouth: If the corners of the mouth droop, products that can lift the corners of the mouth can be recommended.

[0176] Facial contour: If the facial contour shows high cheekbones, products that can modify cheekbones can be recommended.

[0177] It should be noted that this application can also make recommendations based on personality type or facial status information.

[0178] Taking personality type-based recommendations as an example, this application can recommend relevant information to users based on their personality type, such as recommending suitable products and services.

[0179] For example, you can recommend corresponding categories based on a user's personality type, and then recommend suitable products and services based on those categories, such as recommending medical aesthetic products, medical institutions, and professional medical doctors.

[0180] For example, products can be recommended based on MBTI personality types. For instance, ESTJ type users may prefer products that are highly practical, so products that are highly practical can be recommended.

[0181] For example, INTJ users prioritize efficiency and may prefer procedures that can quickly improve facial wrinkles, such as filler injections. These procedures fall under the wrinkle removal and anti-aging category and typically do not require a long recovery period. Therefore, relevant products can be recommended to users based on the wrinkle removal and anti-aging category.

[0182] For example, users with an ESTJ personality might be more inclined to choose cosmetic procedures that deliver tangible results and boost their confidence in social situations. They might opt ​​for procedures that can quickly improve their skin condition, such as IPL (Intense Pulsed Light) skin rejuvenation. These procedures can rapidly improve skin tone and texture, making them more confident in social settings. Therefore, relevant products can be recommended to users based on the IPL skin rejuvenation category.

[0183] Taking recommendations based on facial features as an example, corresponding products are recommended based on scores in three dimensions: perceived age (A), perceived distance (B), and perceived intelligence (C). For example, a user with an A3 rating (appears young) might be interested in products such as anti-wrinkle cream and moisturizing masks, so anti-wrinkle cream and moisturizing masks would be recommended.

[0184] Furthermore, when outputting recommendation information, users' preference data and / or historical recommendation data can be combined to output more personalized recommendation information.

[0185] The recommended information can be displayed in the form of charts and / or text.

[0186] It should be noted that the recommendation information corresponding to facial status information and / or personality traits can also be stored in the data. For example, the data can be encrypted and stored in a local database, or it can be uploaded to a cloud server, supporting remote access and backup. Furthermore, encryption technology and privacy protection measures are used to ensure the security of user information.

[0187] S206. Obtain the user's response based on the recommendation information; generate the execution result corresponding to the user's response based on the response.

[0188] For example, if the recommended information to users includes products such as anti-wrinkle cream and moisturizing masks, the system can obtain the user's selection of these recommended products. If the user selects a moisturizing mask, the system can directly redirect the user to the online store's moisturizing mask purchase page for them to buy.

[0189] For example, if the recommended information to users is related products in the category of phototherapy skin rejuvenation, the system can obtain the user's selection of these recommended products. If the user selects one of the related products in the phototherapy skin rejuvenation category, the system can directly redirect the user to the purchase page of the related product in the online store for the user to make a purchase.

[0190] Based on user feedback and final purchase decisions, user preference data can be analyzed.

[0191] It should be noted that recommendation information and preference data can also be stored, for example, by encrypting the data and storing it in a local database, or by uploading it to a cloud server, supporting remote access and backup, and employing encryption technology and privacy protection measures to ensure user information security. Storing user preference data allows for better delivery of recommendations to users in the future.

[0192] As can be seen from this embodiment, the related technologies cannot provide users with more personalized analysis services; the facial feature analysis technology of the related technologies may not be accurate enough due to factors such as lighting conditions and angle changes, making it difficult to accurately identify subtle changes in expression; and the user interface of the related technologies is not user-friendly and the operation is complicated, making it difficult for users to understand and use; lacking an intuitive feedback mechanism, users cannot quickly obtain useful analysis results. In contrast, the technical solution of this application provides users with personalized services. It can analyze facial state information such as age, distance, and intelligence based on key facial features, and analyze MBTI personality type. Based on the user's MBTI personality type, it can provide customized product and service recommendations. The technical solution of this application also improves the accuracy of facial feature analysis. By combining data from multiple sensors such as images, sound, and ambient light, it significantly improves the accuracy of the MBTI personality test, and by using advanced deep learning algorithms, it more accurately identifies subtle facial changes and expressions, thereby more accurately inferring personality type. The technical solution of this application improves analysis accuracy and enhances user experience. It provides an intuitive and easy-to-use user interface, simplifies the operation process, and allows users to easily understand and use it. Furthermore, a visual feedback mechanism helps users quickly obtain analysis results and suggestions. This application's technical solution also enhances data security by employing encrypted storage and transmission technologies to ensure the security and privacy of user data, and implements a strict privacy protection mechanism to prevent the leakage of personal information. Therefore, this application's technical solution not only provides a more accurate and comprehensive MBTI personality test, but also offers users more personalized and secure services, greatly improving the user experience.

[0193] Figure 3 This is a schematic diagram of the third process of a face information processing method shown in an embodiment of this application.

[0194] See Figure 3 The method includes:

[0195] S301. Obtain user's facial information.

[0196] Among these methods, user facial information can be obtained by collecting facial images.

[0197] S302. Extract key facial features based on the user's facial information.

[0198] Among these features, key facial features can be extracted based on a pre-trained face detection model and user facial information.

[0199] Among them, key facial features may include facial features and / or facial expression features.

[0200] Taking key facial features as an example, this involves extracting key feature points from the facial region, such as the features of the eyes, nose, and mouth. Facial features can include wrinkles, skin condition, and other characteristics. Facial expression features include, for example, smiling and frowning.

[0201] S303. Output the corresponding personality traits based on the key facial features.

[0202] In this application, corresponding user feature information can be output based on key facial features, and the user feature information may include personality traits.

[0203] In this application, the user feature information can be obtained based on a pre-established user feature information prediction model, that is, by inputting key facial features into the prediction model for processing, the corresponding user feature information is obtained; or, the user feature information can also be based on a preset correspondence between key facial features and user feature information, and by matching the user feature information corresponding to the user's facial information through the preset correspondence between key facial features and user feature information.

[0204] To obtain user personality traits more accurately, this application provides a feasible solution for step S303, specifically including:

[0205] Based on the key facial features and the mapping relationship between the key facial features and facial state information, output the facial state information corresponding to the key facial features;

[0206] Based on the facial state information and the mapping relationship between facial state information and personality traits, the personality traits corresponding to the facial state information are output.

[0207] The facial status information in this application may include at least one of the following: perceived age, perceived distance, and perceived intelligence.

[0208] In one embodiment, when outputting personality traits corresponding to facial state information based on facial state information and the mapping relationship between facial state information and personality traits, feature vectors of sense of age, sense of distance, and sense of wisdom can be obtained; and corresponding personality traits can be output based on the feature vectors of sense of age, sense of distance, and sense of wisdom, and the mapping relationship between the feature vectors of sense of age, sense of distance, and sense of wisdom and preset personality assessment dimensions.

[0209] In another embodiment, based on the facial state information and the mapping relationship between the facial state information and personality traits, the personality traits corresponding to the facial state information are output. Alternatively, feature vectors of age perception, distance perception, and intelligence perception can be obtained. Based on the feature vectors of age perception, distance perception, and intelligence perception, facial style features are determined, where facial style features reflect personality traits. Based on the mapping relationship between facial style features and preset personality assessment dimensions, the corresponding personality traits are output.

[0210] This application extracts key facial features from a user's facial information and then outputs corresponding personality traits based on these features. Through this processing, the application can analyze personality traits for the user, thereby enabling more comprehensive feature analysis of facial data, providing users with personality trait analysis, and improving the user experience.

[0211] Furthermore, the personality traits described in this application may be presented in the form of charts and / or text.

[0212] Figure 4 This is a schematic diagram of the fourth process of a face information processing method shown in an embodiment of this application.

[0213] See Figure 4 The method includes:

[0214] S401. Obtain user facial information.

[0215] In this step, facial images are captured to obtain the user's facial information. For example, a camera can be used to capture an image of the user's face. At this point, it's important to ensure image clarity and appropriate lighting conditions. Additionally, camera parameters can be automatically adjusted to adapt to different lighting conditions.

[0216] After acquiring the face image, further image preprocessing can be performed, such as grayscale conversion and noise reduction. Grayscale conversion reduces computational complexity. Gaussian blur filters can be used to remove noise, and edge detection algorithms can enhance boundary information.

[0217] S402. Extract key facial features based on the pre-trained face detection model and user facial information.

[0218] This application can use a pre-trained face detection model to identify the facial region of a face image and extract key facial features from the facial region.

[0219] One approach is to train a face detection model using deep learning methods, such as a CNN (Convolutional Neural Networks)-based model.

[0220] One approach is to use the single-stage detection algorithm of SSD (Single Shot MultiBox Detector) to identify and detect face images.

[0221] It should be noted that the detected facial images can be standardized in size to ensure that all facial images are the same size.

[0222] After identifying the facial region of a face image using a pre-trained face detection model, feature extraction processing is performed, including extracting key facial features from the facial region.

[0223] Among them, key facial features may include facial features and / or facial expression features.

[0224] Taking facial key features as an example, key feature points are extracted from the facial region, such as facial features like the eyes, nose, and mouth. Facial feature features can include wrinkles, skin condition, and other characteristics. Then, facial feature vectors of the key feature points are calculated, including but not limited to facial contours, wrinkles, and blemishes.

[0225] S403. Based on the key facial features and the mapping relationship between the key facial features and facial state information, output the facial state information corresponding to the key facial features.

[0226] Key facial features can include facial features and / or facial expression features. By further analyzing the changing trends of facial expressions, such as smiling and frowning, the corresponding facial state features can be matched more accurately, which is more helpful in assisting subsequent personality analysis.

[0227] The facial status information includes at least one of age perception, distance perception, and intelligence perception. For the relevant definitions and descriptions of age perception, distance perception, and intelligence perception, please refer to the description in step S203, which will not be repeated here.

[0228] S404. Based on the facial state information and the mapping relationship between facial state information and personality traits, output the personality traits corresponding to the facial state information.

[0229] This step involves further data analysis, including personality analysis based on facial status information.

[0230] This step can obtain feature vectors of perceived age, perceived distance, and perceived wisdom; based on the feature vectors of perceived age, perceived distance, and perceived wisdom, and the mapping relationship between these feature vectors and preset personality assessment dimensions, the corresponding personality traits are output.

[0231] The preset personality assessment dimensions can include four dimensions, such as attention direction, cognitive style, judgment style, and lifestyle.

[0232] This application uses MBTI personality type analysis as an example, but is not limited to it.

[0233] Among them, based on the combination of sense of age (A), sense of distance (B), and sense of wisdom (C), the corresponding personality traits can be derived, such as the corresponding MBTI personality type.

[0234] This application can output corresponding personality traits based on the combination of sense of age, sense of distance, and sense of intelligence, and the mapping relationship between the combination and the four personality assessment dimensions of MBTI.

[0235] Among them, feature vectors of sense of age, sense of distance, and sense of wisdom can be obtained, forming a combination of feature vectors. Based on the combination of feature vectors of sense of age, sense of distance, and sense of wisdom, and the mapping relationship between the combination of feature vectors of sense of age, sense of distance, and sense of wisdom and the four personality assessment dimensions of MBTI, the corresponding personality characteristics are output.

[0236] This process involves obtaining feature vectors for perceived age, distance, and intelligence; determining facial style features based on these feature vectors, where facial style features reflect personality traits; and outputting corresponding personality traits based on the mapping relationship between facial style features and preset personality assessment dimensions.

[0237] For example, if a user displays a lot of smiling, open expressions (B3), appears younger (A3), and appears intelligent (C3), the final combination is A3B3C3.

[0238] Based on the combination of A3B3C3, the generated facial style features are "dignified and composed, with a straight and upright posture, a firm and confident gaze, giving a reliable and mature impression. The facial features are solid, with high cheekbones and a defined chin, all reflecting your determination and confidence."

[0239] Based on the mapping relationship between facial style features and the four personality assessment dimensions of MBTI, the corresponding ESTJ (Extroversion, Sensing, Thinking, Judging) personality type is matched.

[0240] It should be noted that the mapping relationship can be represented by a matching table.

[0241] It should also be noted that for the combination A3B3C3, if it is a woman, the matching face shape can be a cool and sophisticated face; if it is a man, the matching face shape can be a cool and handsome face. The corresponding evaluation keywords are "dignified and steady, upright, with a firm and confident gaze, giving people a reliable and mature feeling. Solid facial features, high cheekbones and a well-defined chin, all reflect your determination and confidence."

[0242] For example, in the combination A3B1C3, B1 represents a negative sense of distance, indicating that the user is very distant. The woman's face shape indicates a high level of maturity, conveying a mature and substantial sense of distance. The man's face shape also indicates a high level of maturity, with a rounded overall outline, but he exudes a sense of distance and wealth. The corresponding evaluation keywords are "reserved, confident, deep eyes, well-defined features, fair skin, high intelligence, and independence".

[0243] It should also be noted that this application may not generate facial style features; instead, it may directly output the corresponding personality features based on the mapping relationship between the combination of feature vectors of sense of age, sense of distance, and sense of intelligence and the four personality assessment dimensions of MBTI.

[0244] For example, if a user displays a lot of smiling, open expressions (B3), appears younger (A3), and appears intelligent (C3), the final combination is A3B3C3.

[0245] Based on the mapping relationship between the combination of A3B3C3 and the four personality assessment dimensions of MBTI, the corresponding ESTJ (Extroverted, Sensing, Reasoning, Judging) personality type is matched.

[0246] When outputting personality traits, a detailed MBTI personality test report can be generated. This report can include scores and suggestions for various indicators, such as scores for Introversion (I) and Extroversion (E), Sensing (S) and Intuition (N), Thinking (T) and Feeling (F), Judging (J) and Perceiving (P), etc.

[0247] Among them, the interpretation of personality types can describe the typical characteristics of each personality type to help users understand their own personality type.

[0248] The interpretation of sense of age, sense of distance, and sense of wisdom can be based on the scores of the three dimensions: sense of age (A), sense of distance (B), and sense of wisdom (C), to help users understand their own sense of age, sense of distance, and sense of wisdom.

[0249] Finally, comprehensive suggestions can be provided to users, including combining scores from three dimensions: personality type and sense of age (A), sense of distance (B), and sense of wisdom (C), to help users better understand themselves and improve their lives.

[0250] In addition, when outputting reports, they can be presented to users in the form of charts and / or text.

[0251] This application can also directly output personality features corresponding to facial key features based on facial key features and the mapping relationship between facial key features and personality features.

[0252] Key facial features can include facial features and / or facial expression features. Facial features can include wrinkles, skin condition, etc. Facial expression features include, for example, smiling, frowning, etc.

[0253] For example, if the detected facial features show fewer wrinkles, tight skin, and facial expressions that show more smiles and openness, as well as more confident and focused expressions, then based on the mapping relationship between these key facial features and the four personality assessment dimensions of MBTI, the corresponding ESTJ (Extroverted, Sensing, Reasoning) personality type can be matched.

[0254] It should be noted that the mapping relationship can be represented by a matching table.

[0255] Corresponding to the aforementioned application function implementation method embodiments, this application also provides a face information processing device, terminal, and corresponding embodiments.

[0256] Figure 5 This is a first structural schematic diagram of a face information processing device shown in an embodiment of this application.

[0257] See Figure 5 A face information processing device 500 includes: an information acquisition module 50, a feature extraction module 51, an analysis and processing module 52, and an information recommendation module 53.

[0258] Information acquisition module 50 is used to acquire user facial information;

[0259] The feature extraction module 51 is used to extract key facial features based on the user's facial information;

[0260] The analysis and processing module 52 is used to output corresponding user feature information based on key facial features. The user feature information includes at least personality features.

[0261] The information recommendation module 53 is used to output recommended information corresponding to the user's characteristic information based on the user's characteristic information.

[0262] The information recommendation module 53, when outputting recommendation information, combines user preference data and / or the user's historical recommendation data. The recommendation information can be displayed in the form of charts and / or text.

[0263] The device provided in this application can analyze personality traits for users and output recommendation information corresponding to those traits. This enables more comprehensive feature analysis and processing of facial data, providing users with personality trait analysis and related recommendation services, thereby improving the user experience.

[0264] Figure 6 This is a second structural schematic diagram of the face information processing device shown in the embodiments of this application.

[0265] See Figure 6 A face information processing device 500 includes: an information acquisition module 50, a feature extraction module 51, an analysis and processing module 52, and an information recommendation module 53.

[0266] The analysis and processing module 52 includes: a face status information analysis module 521 and a personality trait analysis module 522.

[0267] The face state information analysis module 521 is used to output face state information corresponding to the face key features based on the face key features and the mapping relationship between the face key features and face state information.

[0268] The personality trait analysis module 522 is used to output the personality traits corresponding to the facial state information based on the facial state information and the mapping relationship between facial state information and personality traits.

[0269] Among them, key facial features may include facial facial features and facial expression features; the facial state information analysis module 521 can output the corresponding facial state information based on facial facial features and facial expression features, as well as the mapping relationship between facial facial features and facial expression features and facial state information.

[0270] Facial status information includes at least one of the following: age, distance, and intelligence.

[0271] In one embodiment, the personality trait analysis module 522 can acquire feature vectors of sense of age, sense of distance, and sense of wisdom; based on the feature vectors of sense of age, sense of distance, and sense of wisdom, and the mapping relationship between the feature vectors of sense of age, sense of distance, and sense of wisdom and preset personality assessment dimensions, it outputs the corresponding personality traits.

[0272] In another embodiment, the personality feature analysis module 522 can also acquire feature vectors of age perception, distance perception, and intelligence perception; determine facial style features based on the feature vectors of age perception, distance perception, and intelligence perception, wherein facial style features reflect personality traits; and output the corresponding personality features based on the mapping relationship between facial style features and preset personality assessment dimensions.

[0273] In addition to personality traits, user feature information may also include facial status information. The information recommendation module 53 can output recommended products corresponding to user feature information based on facial status information and personality traits.

[0274] Figure 7 This is a third structural schematic diagram of the face information processing device shown in the embodiments of this application.

[0275] See Figure 7 A facial information processing device 700 includes: an information acquisition module 70, a feature extraction module 71, and a personality analysis module 72.

[0276] Information acquisition module 70 is used to acquire user facial information;

[0277] The feature extraction module 71 is used to extract key facial features based on the user's facial information;

[0278] The personality analysis module 72 is used to output corresponding personality traits based on key facial features.

[0279] The device provided in this application can analyze personality traits for users, thereby enabling more comprehensive feature analysis and processing of facial data, providing users with personality trait analysis, and improving the user experience.

[0280] Figure 8 This is a fourth structural schematic diagram of the face information processing device shown in the embodiments of this application.

[0281] See Figure 8 A facial information processing device 700 includes: an information acquisition module 70, a feature extraction module 71, and a personality analysis module 72.

[0282] The personality analysis module 72 includes: a face state information analysis module 721 and a personality trait analysis module 722.

[0283] The face state information analysis module 721 is used to output face state information corresponding to the face key features based on the face key features and the mapping relationship between the face key features and face state information.

[0284] The personality trait analysis module 722 is used to output the personality traits corresponding to the facial state information based on the facial state information and the mapping relationship between the facial state information and personality traits.

[0285] The facial status information may include at least one of the following: perceived age, perceived distance, and perceived intelligence.

[0286] In one embodiment, the personality trait analysis module 722 can acquire feature vectors of sense of age, sense of distance, and sense of wisdom; based on the feature vectors of sense of age, sense of distance, and sense of wisdom, and the mapping relationship between the feature vectors of sense of age, sense of distance, and sense of wisdom and preset personality assessment dimensions, it outputs the corresponding personality traits.

[0287] In another embodiment, the personality feature analysis module 722 can also acquire feature vectors of age perception, distance perception, and intelligence perception; determine facial style features based on the feature vectors of age perception, distance perception, and intelligence perception, wherein facial style features reflect personality traits; and output the corresponding personality features based on the mapping relationship between facial style features and preset personality assessment dimensions.

[0288] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated further here.

[0289] Figure 9 This is a schematic diagram of the terminal structure shown in an embodiment of this application.

[0290] See Figure 9 The terminal 1000 includes a memory 1010 and a processor 1020.

[0291] The processor 1020 can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0292] Memory 1010 may include various types of storage units, such as system memory, read-only memory (ROM), and permanent storage devices. ROM may store static data or instructions required by processor 1020 or other modules of the computer. Permanent storage devices may be read-write storage devices. Permanent storage devices may be non-volatile storage devices that retain stored instructions and data even when the computer is powered off. In some embodiments, permanent storage devices use mass storage devices (e.g., magnetic or optical disks, flash memory) as permanent storage devices. In other embodiments, permanent storage devices may be removable storage devices (e.g., floppy disks, optical drives). System memory may be a read-write storage device or a volatile read-write storage device, such as dynamic random access memory. System memory may store some or all of the instructions and data required by the processor during operation. Furthermore, memory 1010 may include any combination of computer-readable storage media, including various types of semiconductor memory chips (e.g., DRAM, SRAM, SDRAM, flash memory, programmable read-only memory), and disks and / or optical disks may also be used. In some embodiments, the memory 1010 may include a removable storage device that is readable and / or writable, such as a laser disc (CD), a read-only digital multifunction optical disc (e.g., DVD-ROM, dual-layer DVD-ROM), a read-only Blu-ray disc, a high-density optical disc, a flash memory card (e.g., SD card, mini SD card, Micro-SD card, etc.), a magnetic floppy disk, etc. Computer-readable storage media do not contain carrier waves or transient electronic signals transmitted wirelessly or via wired connections.

[0293] The memory 1010 stores executable code, which, when processed by the processor 1020, can cause the processor 1020 to execute part or all of the methods described above.

[0294] Furthermore, the method according to this application can also be implemented as a computer program or computer program product, which includes computer program code instructions for performing some or all of the steps in the method described above.

[0295] Alternatively, this application may be implemented as a computer-readable storage medium (or a non-transitory machine-readable storage medium or a machine-readable storage medium) storing executable code (or computer program or computer instruction code) thereon, which, when executed by a processor of an electronic device (or server, etc.), causes the processor to perform part or all of the steps of the methods described above according to this application.

[0296] The various embodiments of this application have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical application, or improvement of the technology in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A face information processing method, characterized by, The method comprises: obtaining user face information; extracting face key features according to the user face information; outputting corresponding user feature information according to the face key features, the user feature information at least containing personality characteristics; outputting recommendation information corresponding to the user feature information according to the user feature information.

2. The method of claim 1, wherein, The user feature information further comprises face state information, and the outputting of the recommendation information corresponding to the user feature information according to the user feature information comprises: outputting recommended products corresponding to the user feature information according to the face state information and the personality characteristics.

3. A face information processing method characterized by comprising: The method comprises: obtaining user face information; extracting face key features according to the user face information; outputting corresponding personality characteristics according to the face key features.

4. The method of claim 3, wherein, The outputting of the personality characteristics corresponding to the face key features according to the face key features comprises: outputting face state information corresponding to the face key features according to the face key features and the mapping relationship between the face key features and the face state information; outputting personality characteristics corresponding to the face state information according to the face state information and the mapping relationship between the face state information and the personality characteristics.

5. The method of claim 4, wherein: the face state information comprises at least one of age feeling, distance feeling and wisdom feeling; the outputting of the personality characteristics corresponding to the face state information according to the face state information and the mapping relationship between the face state information and the personality characteristics comprises: obtaining a feature vector of the age feeling, the distance feeling and the wisdom feeling; outputting corresponding personality characteristics according to the feature vector of the age feeling, the distance feeling and the wisdom feeling and the mapping relationship between the feature vector of the age feeling, the distance feeling and the wisdom feeling and a preset personality evaluation dimension.

6. The method of claim 4, wherein: the face state information comprises at least one of age feeling, distance feeling and wisdom feeling; the outputting of the personality characteristics corresponding to the face state information according to the face state information and the mapping relationship between the face state information and the personality characteristics comprises: obtaining a feature vector of the age feeling, the distance feeling and the wisdom feeling; determining a face style feature according to the feature vector of the age feeling, the distance feeling and the wisdom feeling, wherein the face style feature reflects personality characteristics; outputting corresponding personality characteristics according to the mapping relationship between the face style feature and a preset personality evaluation dimension.

7. A face information processing apparatus characterized by comprising: The device comprises: an information acquisition module configured to obtain user face information; a feature extraction module configured to extract face key features according to the user face information; an analysis processing module configured to output corresponding user feature information according to the face key features, the user feature information at least containing personality characteristics; an information recommendation module configured to output recommendation information corresponding to the user feature information according to the user feature information.

8. A face information processing apparatus characterized by comprising: The device comprises: an information acquisition module configured to obtain user face information; a feature extraction module configured to extract face key features according to the user face information; A personality analysis module is configured to output corresponding personality characteristics according to the face key features.

9. A terminal, characterized by comprising: The method comprises the following steps: A processor; And A memory having executable code stored thereon, which, when executed by the processor, causes the processor to perform the method of any one of claims 1-6. 10.A computer readable storage medium having executable code stored thereon, which, when executed by a processor of an electronic device, causes the processor to perform the method of any one of claims 1-6. ​

Images