Face aging prediction method and apparatus, electronic device, storage medium, and program product

Abstract

A face aging prediction method and apparatus, an electronic device, a storage medium, and a program product. The method comprises: obtaining a face image of a target user, and real-life information and hypothetical life information of the target user; on the basis of the face image and the real-life information, performing aging prediction on the target user to obtain an aging prediction result of the target user under real conditions; and on the basis of the face image and the hypothetical life information, performing aging prediction on the target user to obtain an aging prediction result of the target user under hypothetical conditions.

Description

Face aging prediction method and device, electronic equipment, storage medium and program product TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of computer, and particularly relates to a face aging prediction method, a face aging prediction device, an electronic equipment, a computer readable storage medium and a computer program product. BACKGROUND

[0002] With the rapid development of technology and the improvement of people's living standards, people pay more and more attention to personal health and appearance maintenance. Among them, face aging, as part of the natural aging process of the human body, not only affects the appearance of individuals, but also reflects their overall health status. Therefore, accurately predicting the face aging process is of great significance for individuals to develop targeted health management and beauty plans. SUMMARY

[0003] The present disclosure provides a face aging prediction technical solution.

[0004] According to an aspect of the present disclosure, a face aging prediction method is provided, comprising:

[0005] obtaining a face image of a target user, actual life information of the target user and hypothetical life information;

[0006] performing aging prediction on the target user according to the face image and the actual life information to obtain an aging prediction result of the target user under actual conditions;

[0007] performing aging prediction on the target user according to the face image and the hypothetical life information to obtain an aging prediction result of the target user under hypothetical conditions.

[0008] In a possible implementation, the aging prediction result under actual conditions comprises an aging prediction image under actual conditions, and the aging prediction result under hypothetical conditions comprises an aging prediction image under hypothetical conditions.

[0009] After obtaining the aging prediction result under actual conditions and the aging prediction result under hypothetical conditions, the method further comprises:

[0010] displaying the aging prediction image under actual conditions and the aging prediction image under hypothetical conditions.

[0011] In a possible implementation, the aging prediction image under actual conditions comprises aging prediction images of different ages under actual conditions, and the aging prediction image under hypothetical conditions comprises aging prediction images of different ages under hypothetical conditions.

[0012] In a possible implementation, the aging prediction image under actual conditions comprises an aging prediction three-dimensional image under actual conditions, and the aging prediction image under assumed conditions comprises an aging prediction three-dimensional image under assumed conditions.

[0013] In a possible implementation, the face image comprises a three-dimensional face image of the target user.

[0014] The face image of the target user is obtained by acquiring a two-dimensional face image of the target user, and generating a three-dimensional face image of the target user according to the two-dimensional face image.

[0015] In a possible implementation, the assumed life information comprises assumed optimal life information, and the aging prediction result under assumed conditions comprises a slowest aging prediction result corresponding to the target user.

[0016] The aging prediction of the target user is performed according to the face image and the assumed life information, to obtain the aging prediction result of the target user under assumed conditions, comprising:

[0017] The aging prediction of the target user is performed according to the face image and the assumed optimal life information, to obtain the slowest aging prediction result.

[0018] In a possible implementation, the assumed life information comprises optimal information of at least one specified lifestyle item, and the aging prediction result under assumed conditions comprises an improved aging prediction result corresponding to the target user, which represents an aging prediction result corresponding to the target user after the at least one specified lifestyle item is improved to be optimal.

[0019] The aging prediction of the target user is performed according to the face image and the assumed life information, to obtain the aging prediction result of the target user under assumed conditions, comprising:

[0020] The aging prediction of the target user is performed according to the face image, the optimal information of the at least one specified lifestyle item, and information of non-specified lifestyle items in the actual life information, to obtain the improved aging prediction result.

[0021] In a possible implementation, the assumed life information comprises assumed worst life information, and the aging prediction result under assumed conditions comprises a fastest aging prediction result corresponding to the target user.

[0022] The aging prediction of the target user is performed according to the face image and the assumed life information, and the aging prediction result of the target user under the assumed condition is obtained.

[0023] The aging prediction of the target user is performed according to the face image and the assumed worst life information, and the fastest aging prediction result is obtained.

[0024] In a possible implementation, the assumed life information includes worst information of at least one specified lifestyle item, and the aging prediction result under the assumed condition includes an accelerated aging prediction result corresponding to the target user, which represents an aging prediction result corresponding to the target user when the at least one specified lifestyle item is deteriorated to the worst;

[0025] The aging prediction of the target user is performed according to the face image and the assumed life information, and the aging prediction result of the target user under the assumed condition is obtained.

[0026] The aging prediction of the target user is performed according to the face image, the worst information of the at least one specified lifestyle item, and the information of the non-specified lifestyle item in the actual life information, and the accelerated aging prediction result is obtained.

[0027] In a possible implementation, the method further includes:

[0028] For the aging prediction images under the actual condition and the aging prediction images under the assumed condition of the same age, the face regions with differences in the aging prediction images under the actual condition and the aging prediction images under the assumed condition are highlighted.

[0029] In a possible implementation, the actual life information includes at least part of the following: actual life habit information, actual health status information, actual work status information, and actual living environment information.

[0030] The assumed life information includes at least part of the following: assumed life habit information, assumed health status information, assumed work status information, and assumed living environment information.

[0031] In a possible implementation, the method further includes:

[0032] A time axis corresponding to an aging prediction age range is displayed.

[0033] In response to any time point in the time axis being selected, the aging prediction images under the actual condition and the aging prediction images under the assumed condition corresponding to the age of the selected time point are displayed.

[0034] In a possible implementation, the method further includes:

[0035] transforming the aging prediction image in response to a transformation operation on any aging prediction image;

[0036] wherein the aging prediction image is based on an actual condition or a hypothetical condition; and the transformation operation includes at least one of the following: zoom-in operation, zoom-out operation, and rotation operation.

[0037] In a possible implementation, the transforming the aging prediction image in response to a transformation operation on any aging prediction image includes:

[0038] transforming the aging prediction image based on an actual condition and the aging prediction image based on a hypothetical condition corresponding to the same age in response to a transformation operation on any aging prediction image based on an actual condition;

[0039] or,

[0040] transforming the aging prediction image based on a hypothetical condition and the aging prediction image based on an actual condition corresponding to the same age in response to a transformation operation on any aging prediction image based on a hypothetical condition.

[0041] In a possible implementation, the method further includes:

[0042] obtaining genetic information of the target user;

[0043] correcting the aging prediction result based on an actual condition according to the genetic information to obtain a corrected aging prediction result based on an actual condition of the target user.

[0044] In a possible implementation, the obtaining genetic information of the target user includes:

[0045] performing genetic analysis on a saliva sample of the target user to obtain the genetic information of the target user.

[0046] In a possible implementation, after the obtaining the aging prediction result based on an actual condition of the target user, the method further includes:

[0047] generating recommended lifestyle information corresponding to the target user according to the aging prediction result based on an actual condition and the actual life information;

[0048] output the recommended lifestyle information.

[0049] In a possible implementation, after the obtaining of the aging prediction result of the target user under the actual condition, the method further includes:

[0050] generating recommended cosmetic information corresponding to the target user according to the aging prediction result under the actual condition;

[0051] outputting the recommended cosmetic information.

[0052] In a possible implementation, the aging prediction image corresponding to each age of the target user includes:

[0053] the face concave-convex prediction image corresponding to the age of the target user, wherein the face concave-convex prediction image is used to show the concave-convex degree of different regions of the face.

[0054] According to an aspect of the present disclosure, a face aging prediction device is provided, which includes:

[0055] a first obtaining module configured to obtain a face image of a target user, actual life information and assumed life information of the target user;

[0056] a first aging prediction module configured to perform aging prediction on the target user according to the face image and the actual life information, and obtain an aging prediction result of the target user under an actual condition;

[0057] a second aging prediction module configured to perform aging prediction on the target user according to the face image and the assumed life information, and obtain an aging prediction result of the target user under an assumed condition.

[0058] In a possible implementation, the aging prediction result under the actual condition includes an aging prediction image under the actual condition, and the aging prediction result under the assumed condition includes an aging prediction image under the assumed condition.

[0059] The device further includes:

[0060] a first display module configured to display the aging prediction image under the actual condition and the aging prediction image under the assumed condition.

[0061] In a possible implementation, the aging prediction image under the actual condition includes aging prediction images of different ages under the actual condition, and the aging prediction image under the assumed condition includes aging prediction images of different ages under the assumed condition.

[0062] In a possible implementation, the aging prediction image under the actual condition comprises an aging prediction three-dimensional image under the actual condition, and the aging prediction image under the assumed condition comprises an aging prediction three-dimensional image under the assumed condition.

[0063] In a possible implementation, the face image comprises a three-dimensional face image of the target user.

[0064] The first obtaining module is configured to: acquire a two-dimensional face image of a target user; and generate a three-dimensional face image of the target user according to the two-dimensional face image.

[0065] In a possible implementation, the assumed life information comprises assumed optimal life information, and the aging prediction result under the assumed condition comprises a slowest aging prediction result corresponding to the target user.

[0066] The second aging prediction module is configured to:

[0067] The target user is aging predicted according to the face image and the assumed optimal life information, to obtain the slowest aging prediction result.

[0068] In a possible implementation, the assumed life information comprises optimal information of at least one specified lifestyle item, and the aging prediction result under the assumed condition comprises an improved aging prediction result corresponding to the target user, the improved aging prediction result indicating an aging prediction result corresponding to the target user after the target user improves the at least one specified lifestyle item to be optimal.

[0069] The second aging prediction module is configured to:

[0070] The target user is aging predicted according to the face image, the optimal information of the at least one specified lifestyle item, and information of non-specified lifestyle items in the actual life information, to obtain the improved aging prediction result.

[0071] In a possible implementation, the assumed life information comprises assumed worst life information, and the aging prediction result under the assumed condition comprises a fastest aging prediction result corresponding to the target user.

[0072] The second aging prediction module is configured to:

[0073] The target user is aging predicted according to the face image and the assumed worst life information, to obtain the fastest aging prediction result.

[0074] In a possible implementation, the assumed life information includes at least one worst information of a specified lifestyle item, and the accelerated aging prediction result based on the assumption includes an accelerated aging prediction result corresponding to the target user, which represents an aging prediction result corresponding to the target user after the target user deteriorates the at least one specified lifestyle item to the worst.

[0075] The second aging prediction module is configured to:

[0076] According to the face image, the worst information of the at least one specified lifestyle item, and the information of the non-specified lifestyle item in the actual life information, the target user is subjected to aging prediction to obtain the accelerated aging prediction result.

[0077] In a possible implementation, the device further includes:

[0078] The highlighting module is configured to highlight, for the aging prediction image based on the actual condition and the aging prediction image based on the assumption for the same age, a facial region in which the aging prediction image based on the actual condition and the aging prediction image based on the assumption are different.

[0079] In a possible implementation, the actual life information includes at least part of the following: actual life habit information, actual health condition information, actual work condition information, and actual life environment information.

[0080] The assumed life information includes at least part of the following: assumed life habit information, assumed health condition information, assumed work condition information, and assumed life environment information.

[0081] In a possible implementation, the device further includes:

[0082] The second display module is configured to display a time axis corresponding to an aging prediction age range.

[0083] The third display module is configured to, in response to any time point in the time axis being selected, display an aging prediction image based on the actual condition and an aging prediction image based on the assumption corresponding to an age corresponding to the selected time point.

[0084] In a possible implementation, the device further includes:

[0085] The transformation module is configured to, in response to a transformation operation for any aging prediction image, perform transformation processing on the aging prediction image.

[0086] The aging prediction image is based on an actual condition or a hypothetical condition. The transformation operation includes at least one of the following: magnification, reduction, rotation.

[0087] In a possible implementation, the transformation module is configured to:

[0088] In response to a transformation operation on any actual-condition-based aging prediction image, the actual-condition-based aging prediction image and a hypothetical-condition-based aging prediction image corresponding to the same age of the actual-condition-based aging prediction image are synchronously processed.

[0089] Alternatively,

[0090] In response to a transformation operation on any actual-condition-based aging prediction image, the actual-condition-based aging prediction image and a hypothetical-condition-based aging prediction image corresponding to the same age of the actual-condition-based aging prediction image are synchronously processed.

[0091] In a possible implementation, the device further includes:

[0092] The second obtaining module is configured to obtain genetic information of the target user.

[0093] The correction module is configured to correct the actual-condition-based aging prediction result according to the genetic information, to obtain a corrected actual-condition-based aging prediction result of the target user.

[0094] In a possible implementation, the second obtaining module is configured to:

[0095] The genetic information of the target user is obtained by performing genetic analysis on a saliva sample of the target user.

[0096] In a possible implementation, the device further includes:

[0097] The first generating module is configured to generate recommended lifestyle information corresponding to the target user according to the actual-condition-based aging prediction result and the actual lifestyle information.

[0098] The first output module is configured to output the recommended lifestyle information.

[0099] In a possible implementation, after the actual-condition-based aging prediction result of the target user is obtained, the method further includes:

[0100] a second generation module configured to generate recommended cosmetic information corresponding to the target user according to the aging prediction result under the actual condition;

[0101] a second output module configured to output the recommended cosmetic information.

[0102] In a possible implementation, the aging prediction image corresponding to the target user at any age includes:

[0103] the facial concave-convex prediction image corresponding to the target user at the age, wherein the facial concave-convex prediction image is used to show the concave-convex degree of different regions of the face.

[0104] According to an aspect of the present disclosure, an electronic device is provided, comprising: one or more processors; a memory for storing executable instructions; wherein the one or more processors are configured to invoke the executable instructions stored in the memory to perform the above method.

[0105] According to an aspect of the present disclosure, a computer readable storage medium is provided, which stores computer program instructions, and the computer program instructions are executed by a processor to implement the above method.

[0106] According to an aspect of the present disclosure, a computer program product is provided, comprising computer readable code, or a non-volatile computer readable storage medium carrying computer readable code, when the computer readable code is run in an electronic device, a processor in the electronic device executes the above method.

[0107] In the embodiments of the present disclosure, by obtaining a face image of a target user, actual life information and assumed life information of the target user, performing aging prediction on the target user according to the face image and the actual life information to obtain an aging prediction result of the target user under an actual condition, and performing aging prediction on the target user according to the face image and the assumed life information to obtain an aging prediction result of the target user under an assumed condition, the user can obtain customized aging prediction based on his / her facial features and lifestyle, which helps to more accurately understand the future appearance change of the individual. In addition, by obtaining the aging prediction result of the target user under the actual condition and the aging prediction result of the target user under the assumed condition, the user can understand the potential impact of different lifestyles on the aging process, and increase the awareness of the importance of a healthy lifestyle.

[0108] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, but not limiting the present disclosure.

[0109] Other features and aspects of the present disclosure will become apparent from a detailed description of exemplary embodiments with reference to the following drawings. BRIEF DESCRIPTION OF DRAWINGS

[0110] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and serve to explain the technical solutions of the present disclosure together with the specification.

[0111] FIG. 1 shows a flowchart of a face aging prediction method according to an embodiment of the present disclosure.

[0112] FIG. 2 shows a schematic diagram of converting a two-dimensional face image of a target user into a three-dimensional face image in the face aging prediction method according to an embodiment of the present disclosure.

[0113] FIG. 3 shows a schematic diagram of an aging prediction image of a target user at different ages based on actual conditions in the face aging prediction method according to an embodiment of the present disclosure.

[0114] FIG. 4 shows a schematic diagram of the fastest aging prediction result corresponding to a target user in the face aging prediction method according to an embodiment of the present disclosure.

[0115] FIG. 5 shows a schematic diagram of a time axis corresponding to an aging prediction age range in the face aging prediction method according to an embodiment of the present disclosure.

[0116] FIG. 6 shows a schematic diagram of a face concave-convex prediction image in the face aging prediction method according to an embodiment of the present disclosure.

[0117] FIG. 7 shows another schematic diagram of a face concave-convex prediction image in the face aging prediction method according to an embodiment of the present disclosure.

[0118] FIG. 8 shows a schematic diagram of the slowest aging prediction result 20 years later, 30 years later and 40 years later based on the assumed optimal life information, and the fastest aging result 20 years later, 30 years later and 40 years later based on the assumed worst life information in the face aging prediction method according to an embodiment of the present disclosure.

[0119] FIG. 9 shows a block diagram of a face aging prediction device according to an embodiment of the present disclosure.

[0120] FIG. 10 shows a block diagram of an electronic device 1900 according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0121] Various exemplary embodiments, features and aspects of the present disclosure will be described in detail below with reference to the accompanying drawings. The same reference numbers in the drawings represent functionally the same or similar elements. Although various aspects of the embodiments are shown in the drawings, the drawings are not necessarily drawn to scale unless specifically indicated.

[0122] The term "exemplary" is used herein to mean "serving as an example, instance, or illustration." Any embodiment described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments.

[0123] The term "and / or", merely describes association relationship of associated objects, and means that three relationships can exist, for example, A and / or B can mean that A exists alone, A and B exist simultaneously, and B exists alone. In addition, the term "at least one" herein means any one of multiple or any combination of at least two of multiple, for example, at least one of A, B and C includes any one or more elements selected from the set consisting of A, B and C.

[0124] In addition, in order to better illustrate the present disclosure, numerous specific details are given in the specific embodiments below. Those skilled in the art should understand that the present disclosure can also be implemented without certain specific details. In some examples, methods, means, elements and circuits well known to those skilled in the art are not described in detail, in order to highlight the main idea of the present disclosure.

[0125] The embodiments of the present disclosure provide a face aging prediction method and device, electronic equipment, storage medium and program product, which obtain a face image of a target user, actual life information and assumed life information of the target user, perform aging prediction on the target user according to the face image and the actual life information to obtain an aging prediction result of the target user based on actual conditions, and perform aging prediction on the target user according to the face image and the assumed life information to obtain an aging prediction result of the target user based on assumed conditions. Thus, the user can obtain customized aging prediction based on his / her facial features and lifestyle, which helps to more accurately understand the future appearance change of the individual. In addition, by obtaining the aging prediction result of the target user based on actual conditions and the aging prediction result of the target user based on assumed conditions, the user can understand the potential impact of different lifestyles on the aging process and increase the awareness of the importance of a healthy lifestyle.

[0126] The face aging prediction method provided by the embodiments of the present disclosure will be described in detail below in combination with the drawings.

[0127] FIG. 1 shows a flowchart of a face aging prediction method provided by an embodiment of the present disclosure. In a possible implementation, an execution subject of the face aging prediction method can be a face aging prediction apparatus, for example, the face aging prediction method can be executed by a terminal device or a server or other electronic device. Wherein, the terminal device can be a user equipment (UE), a mobile device, a user terminal, a terminal, a cellular phone, a cordless phone, a personal digital assistant (PDA), a handheld device, a computing device, a vehicle-mounted device, or a wearable device, etc. In some possible implementations, the face aging prediction method can be implemented by a processor invoking computer readable instructions stored in a memory. As shown in FIG. 1, the face aging prediction method includes steps S11 to S13.

[0128] In step S11, a face image of a target user, actual life information of the target user, and assumed life information are obtained.

[0129] In step S12, the target user is predicted to age according to the face image and the actual life information, to obtain an aging prediction result of the target user based on actual conditions.

[0130] In step S13, the target user is predicted to age according to the face image and the assumed life information, to obtain an aging prediction result of the target user based on assumed conditions.

[0131] In an embodiment of the present disclosure, the target user can represent any user participating in face aging prediction, that is, the target user can be an individual whose face aging process is predicted. In an embodiment of the present disclosure, the personal information (such as the face image and the actual life information) of the target user can be obtained on the premise of obtaining the authorization of the target user, to perform personalized face aging prediction on the target user.

[0132] In an embodiment of the present disclosure, the face image of the target user, the actual life information of the target user, and the assumed life information can be obtained, and the aging prediction can be performed based on the face image of the target user, the actual life information of the target user, and the assumed life information.

[0133] Wherein, the face image of the target user can represent a face image of the target user. The face image can be used to capture the current facial features of the target user, including but not limited to facial structure, skin texture, wrinkles, etc. These features can be subsequently used to analyze and predict possible facial changes over time. In an embodiment of the present disclosure, the face image of the target user can include a two-dimensional face image and / or a three-dimensional face image of the target user.

[0134] In a possible implementation, the face image includes a three-dimensional face image of the target user; and the obtaining of the face image of the target user includes: acquiring a two-dimensional face image of the target user; and generating the three-dimensional face image of the target user according to the two-dimensional face image.

[0135] In this implementation, the two-dimensional face image of the target user can be converted into a three-dimensional face image by using image processing and computer vision technologies. The conversion process can include technologies such as depth estimation, feature point matching, and three-dimensional reconstruction. Through the conversion process, the three-dimensional face image of the target user can be generated. The three-dimensional face image can more accurately reflect the facial features and structure of the target user.

[0136] FIG. 2 shows a schematic diagram of converting a two-dimensional face image of a target user into a three-dimensional face image in the face aging prediction method provided by the embodiments of the present disclosure. In FIG. 2, a two-dimensional face image of a target user is shown, and a three-dimensional face image of the target user is shown in a front view, a 45° view, and a side view.

[0137] In this implementation, compared with directly acquiring a three-dimensional face image of the target user, it is more convenient and practical to require the target user to provide a two-dimensional face image, because the two-dimensional face image is easier to acquire and process. In addition, by converting the two-dimensional face image of the target user into a three-dimensional face image, the depth information of the face of the target user can be provided, for example, can include details such as facial concave-convex and skin texture, thereby improving the accuracy and reliability of the face aging prediction.

[0138] In this implementation, after the three-dimensional face image of the target user is generated, the target user can be predicted for aging according to the three-dimensional face image and the actual life information, to obtain the aging prediction result of the target user based on the actual condition, and the target user can be predicted for aging according to the three-dimensional face image and the assumed life information, to obtain the aging prediction result of the target user based on the assumed condition.

[0139] In another possible implementation, the face image includes a two-dimensional face image of the target user, and in the process of predicting the corresponding aging prediction result of the target user based on the actual condition and the aging prediction result of the target user based on the assumed condition based on the two-dimensional face image of the target user, the three-dimensional face image of the target user can not be generated.

[0140] In another possible implementation, the face image includes a three-dimensional face image of the target user. In this implementation, the three-dimensional face image of the target user can be directly acquired for face aging prediction.

[0141] In the embodiments of the present disclosure, the actual life information of the target user can be a series of data related to the daily life habits and behavior patterns of the target user. The actual life information can be collected through questionnaires, health check-ups, life habit interviews, etc. By analyzing how lifestyle factors affect the aging process, the facial feature changes of the target user at different ages in the future can be more accurately predicted.

[0142] The hypothetical life information can be a reference standard or hypothetical condition provided for the face aging prediction model, used to simulate and predict the possible aging process of the target user under a certain lifestyle. For example, the hypothetical life information can be a set of standard data on aspects such as life habits, health status, working environment, and living environment, which can be used as a reference basis for predicting aging. The hypothetical life information can be used to simulate different life scenarios in the face aging prediction model to evaluate how these factors affect the aging process of individuals.

[0143] In one possible implementation, the hypothetical life information can include optimal lifestyle information (i.e., assuming that the individual has all possible life habits and conditions that can delay aging) and / or worst lifestyle information (i.e., assuming that the individual's life habits and conditions can accelerate the aging process).

[0144] In one possible implementation, the actual life information includes at least part of the following: actual life habit information, actual health status information, actual working condition information, and actual living environment information; the hypothetical life information includes at least part of the following: hypothetical life habit information, hypothetical health status information, hypothetical working condition information, and hypothetical living environment information.

[0145] For example, the actual life habit information can include at least part of the following: smoking habit information, drinking habit information, eating habit information, sleeping habit information, exercise habit information, and tea drinking habit information. The smoking habit information can include the number of cigarettes smoked per day, the length of smoking, etc. The drinking habit information can include the frequency, type, and amount of alcohol consumption. The eating habit information can relate to the types of daily diet, the balance of nutrient intake, the processing method of food, etc. The sleeping habit information can include the sleep time, sleep quality, and sleep regularity per day. The exercise habit information can relate to the frequency, type, and duration of exercise per week. The tea drinking habit information can include the frequency, type, and time of tea drinking, etc.

[0146] For example, the actual health condition information can include at least part of information in medical history, fertility history, mental condition information, oral health information, etc. Among them, the medical history can include the disease history, chronic disease condition, operation history, etc. of the individual. The fertility history can include the number of births, the age of birth, etc. The mental condition information can relate to the stress level, emotional state, mental illness history, etc. of the individual. The oral health information can include the health condition of the teeth, oral hygiene habits, etc.

[0147] For example, the actual working condition information can include at least part of information in working environment information, physical workload information, working time length, etc. Among them, the working environment information can include environmental factors of the workplace, such as air circulation, noise level, radiation, etc. The physical workload information can include the intensity and frequency of physical labor in work. The working time length can include the number of hours worked per week, overtime work, etc.

[0148] For example, the actual living environment information can include at least part of information in family economic condition information, family smoking habit information, living environment information, social activity information, etc. Among them, the family economic condition information can include the income level of the family, economic pressure, etc. The family smoking habit information can be information indicating whether the family members have smoking habits. The living environment information can include the type of residence, the surrounding environment, the greening condition, etc. The social activity information can include the frequency and type of participating in social activities, etc.

[0149] The specific types of the assumed living habit information can be the same as or similar to the actual living habit information, the specific types of the assumed health condition information can be the same as or similar to the actual health condition information, the specific types of the assumed working condition information can be the same as or similar to the actual working condition information, and the specific types of the assumed living environment information can be the same as or similar to the actual living environment information, which will not be repeated here.

[0150] In the embodiments of the present disclosure, the aging prediction of the target user can be performed according to the face image and the actual living information, and the aging prediction result of the target user based on the actual condition is obtained. In one possible implementation, a face aging prediction model can be used, which can predict the change of the face of the target user over time according to the face image and the actual living information. The face aging prediction model can be based on a machine learning algorithm, which can learn the pattern of aging from a large amount of data.

[0151] Among them, the aging prediction result based on the actual condition can represent the result obtained by aging prediction based on the actual lifestyle information (i.e. actual living information) of the user. The aging prediction result based on the actual condition can be obtained by analyzing and calculating the face image and the actual living information of the target user.

[0152] In one possible implementation, the data form of the aging prediction result under actual conditions can include at least part of image data (e.g., based on an aging prediction image under actual conditions), a numerical score, statistical data, a text description, a probability prediction, a feature vector, etc. Among them, the image data can show the possible appearance of the target user at a certain age. The numerical score can include a score of the degree of skin aging, such as a score system from 1 to 10, reflecting different stages of aging. The statistical data may, for example, include statistical indicators such as the number of predicted wrinkles, depth, skin elasticity, etc. The text description can be a written description of the predicted aging features, such as skin texture, wrinkle distribution, etc. The probability prediction can give the probability of the occurrence of certain aging features, such as dark circles, age spots, etc. In addition, in the machine learning model, the prediction result can be represented in the form of a feature vector to capture the key features of facial aging.

[0153] In one possible implementation, the method further comprises: obtaining genetic information of the target user; and correcting the aging prediction result under actual conditions according to the genetic information to obtain a corrected aging prediction result under actual conditions of the target user.

[0154] In this implementation, the genetic information of the target user can represent the genetic background data of the target user. Genetic information is crucial for understanding the differences in individual aging processes, as genetic factors greatly affect human physiological characteristics and aging speed.

[0155] In this implementation, the corrected aging prediction result under actual conditions can represent an updated version of the prediction result obtained by further optimizing and adjusting the aging prediction result under actual conditions using additional information such as genetic information.

[0156] As an example of this implementation, the genetic information and the aging prediction result under actual conditions can be input into a correction model, and the corrected aging prediction result under actual conditions can be output by the correction model.

[0157] In this implementation, the genetic information can be used to correct at least part of the results of fine lines and wrinkles, skin laxity and sagging, facial hollows, pigmentation and discoloration, skin texture and pore size, facial skeletal structure changes, hair graying, etc. in the aging prediction result under actual conditions.

[0158] The correction process helps to reduce the prediction error due to differences in genetic background, making the result closer to the actual aging situation of the target user. The corrected aging prediction result under actual conditions is more personalized, as it takes into account the genetic characteristics of the target user, which play an important role in the aging process of the target user.

[0159] As an example of this implementation, the corrected aging prediction result under actual conditions can include at least part of the data in the corrected image data, refined numerical score, updated statistical data, enhanced textual description, corrected probability prediction, optimized feature vector, etc. Among them, the corrected image data can include a three-dimensional image sequence, for example, can include a three-dimensional face model of the target user at different ages to reflect a more realistic aging process. After considering the genetic information, the preliminary score in the aging prediction result under actual conditions is adjusted, which can provide a more accurate numerical score. The updated statistical data can be a statistical indicator adjusted according to the genetic information, and can include more accurate prediction data. The enhanced textual description can be a detailed description after combining the genetic information, and can include the influence of specific genetic characteristics on the aging process. The corrected probability prediction can be the probability of the occurrence of certain aging features, which can reflect the influence of genetic correction. In the machine learning model, the feature vector can be optimized to more accurately represent the prediction result.

[0160] In a possible implementation, the obtaining the genetic information of the target user comprises: performing genetic analysis on a saliva sample of the target user to obtain the genetic information of the target user.

[0161] In this implementation, first, a saliva sample of the target user can be collected. As an example of this implementation, a store clerk can collect a saliva sample of the target user on site. As another example of this implementation, a saliva collection kit can be provided to the target user, and the target user can collect a certain amount of saliva according to the instructions and return it to the laboratory or detection center.

[0162] In this implementation, after receiving the saliva sample of the target user, DNA (Deoxyribonucleic Acid) can be extracted therefrom. Saliva contains oral epithelial cells, and these cells contain the genetic material of the target user. The extracted DNA can be subjected to specific genetic analysis techniques, such as gene sequencing or specific genetic marker analysis, to identify and analyze the genetic characteristics of the user. The obtained data will be decoded to determine the genetic information of the target user, including gene variations or specific genetic markers that can affect the aging process.

[0163] In this implementation, the genetic information of the individual is obtained by analyzing the DNA in the saliva sample of the target user. Using a saliva sample as the source of genetic information is completely non-invasive and more comfortable and acceptable for the user. In addition, compared with other methods of collecting genetic information, the collection and processing of saliva samples are relatively simple and facilitate large-scale application.

[0164] In other possible implementations, the genetic information of the target user can also be obtained based on a buccal swab, a hair sample, a blood sample, a skin biopsy, a nail sample, a urine sample, and the like, without limitation.

[0165] In another possible implementation, the genetic information can not be used to correct the aging prediction result based on the actual condition.

[0166] In the embodiments of the present disclosure, the target user can be aged predicted based on the face image of the target user and the assumed life information, to obtain an aging prediction result of the target user based on an assumed condition. The aging prediction result based on the assumed condition can represent a prediction result generated based on a specific assumed condition or standard (i.e., assumed life information) in the face aging prediction process. That is, the aging prediction result based on the assumed condition can be the facial aging state of the target user at different age stages calculated by the face aging prediction model according to the assumed life information. The aging prediction result based on the assumed condition can provide reference information for the user, helping the user understand the possible aging appearance of the target user under different lifestyles or conditions. That is, the aging prediction result based on the assumed condition can be used for comparison with the actual lifestyle information of the target user to show the potential impact of lifestyle on the aging process.

[0167] In a possible implementation, the assumed life information includes assumed optimal life information, and the aging prediction result based on the assumed condition includes a slowest aging prediction result corresponding to the target user. The aging prediction of the target user based on the face image and the assumed life information to obtain the aging prediction result of the target user based on the assumed condition includes aging prediction of the target user based on the face image and the assumed optimal life information to obtain the slowest aging prediction result.

[0168] In this implementation, the assumed optimal life information can be a set of idealized lifestyle parameters set as a reference standard in the face aging prediction model. These parameters reflect the optimal combination of healthy and active lifestyle habits, used to simulate and predict how a person's facial aging process might develop if they followed these best practices. The assumed optimal life information can include a series of lifestyle factors believed to have a positive impact on health and aging delay, such as a balanced diet, moderate exercise, adequate sleep, no smoking and drinking habits, and the like. The assumed optimal life information can be used to provide an idealized comparison benchmark to help users understand how their aging process might be different if they followed these healthy habits.

[0169] In this implementation, the assumed optimal life information can be input into the face aging prediction model, which can simulate the aging appearance of the target user under the best living conditions, and obtain the slowest aging prediction result as the aging prediction result based on the assumed conditions.

[0170] The slowest aging prediction result can represent the face aging prediction result of the target user generated based on the assumed optimal life information. The slowest aging prediction result can be a prediction result calculated by the face aging prediction model according to a series of assumed optimal lifestyle factors, such as healthy diet, regular exercise, sufficient sleep, and no bad habits. The slowest aging prediction result can provide an idealized aging process example for the target user, helping them understand the potential positive impact of a healthy lifestyle on delaying aging.

[0171] In some application scenarios, the slowest aging prediction result can also be referred to as the slowest aging prediction result, etc., which is not limited herein.

[0172] In this implementation, the slowest aging prediction result is generated by combining the assumed optimal life information and the face image of the target user, thereby providing the user with an intuitive display of the optimal aging state that can be achieved under an ideal lifestyle, helping the user understand the positive impact of a healthy lifestyle on the aging process. By comparing the actual aging state and the slowest aging prediction result, the user can more clearly recognize the importance of a healthy lifestyle, thereby enhancing the motivation to improve their lifestyle habits.

[0173] In one possible implementation, the assumed life information includes optimal information of at least one specified lifestyle item, and the aging prediction result based on the assumed conditions includes an improved aging prediction result corresponding to the target user, which represents the aging prediction result corresponding to the target user after improving the at least one specified lifestyle item to the optimal state; and the aging prediction of the target user based on the face image and the assumed life information to obtain the aging prediction result of the target user based on the assumed conditions includes: aging prediction of the target user based on the face image, the optimal information of the at least one specified lifestyle item, and the information of the non-specified lifestyle item in the actual life information, to obtain the improved aging prediction result.

[0174] In this implementation, the specified lifestyle item can represent a lifestyle component that is specifically considered in face aging prediction analysis, such as eating habits, exercise frequency, sleep quality, smoking, and drinking, etc. For each specified lifestyle item, an optimal state or standard (i.e., optimal information) can be set, such as moderate exercise or balanced diet, as a basis for predicting ideal aging results.

[0175] The non-designated lifestyle item can represent other lifestyle components of the target user in addition to the lifestyle factors selected for improvement or focus (i.e., the "at least one designated lifestyle item"). For example, the non-designated lifestyle item can include work environment, social activities, personal hobbies, etc.

[0176] In this implementation, only part of the lifestyle information (e.g., sleep) is changed to optimal in predicting the facial aging of the target user, and other items still use the actual lifestyle information. By improving at least one designated lifestyle item to the optimal state, the aging appearance that the target user can achieve under such ideal changes can be predicted, thereby obtaining an improved aging prediction result.

[0177] In this implementation, by combining the actual facial image of the target user and the optimal information of at least one designated lifestyle item, the aging appearance of the target user after improving certain lifestyle factors is predicted, so that the target user not only understands the impact of his current lifestyle on aging, but also anticipates the potential benefits that can be obtained through active lifestyle changes, thereby making a more healthy and beautiful life choice.

[0178] In a possible implementation, the assumed lifestyle information includes preset worst lifestyle information, and the aging prediction result based on the assumed condition includes a fastest aging prediction result corresponding to the target user; and the aging prediction of the target user based on the facial image and the assumed lifestyle information to obtain the aging prediction result of the target user based on the assumed condition includes: aging prediction of the target user based on the facial image and the worst lifestyle information to obtain the fastest aging prediction result.

[0179] In this implementation, the preset worst lifestyle information can be a set of parameters representing unhealthy or harmful lifestyle habits and conditions that can accelerate the aging process. This can include unbalanced diet, lack of exercise, insufficient sleep, smoking, excessive drinking, etc. In this implementation, in the preset worst lifestyle information, the parameters of each lifestyle item can be the worst. The preset worst lifestyle information provides a negative example of aging prediction, which helps users understand and evaluate the impact of their lifestyle choices on long-term health and aging.

[0180] The preset worst lifestyle information can be used in the face aging prediction model to simulate the aging process of an individual under extremely unfavorable conditions, thereby providing reference information for aging prediction. By using the preset worst lifestyle information as an input condition, the face aging prediction model can simulate the appearance of the target user under the influence of poor living habits. Using the preset worst lifestyle information, the face aging prediction model can generate a prediction result (i.e., a fastest aging prediction result) showing the accelerated aging that the target user may face if he or she continues to live in this way.

[0181] The fastest aging prediction result can represent a prediction result of the future facial aging state of the target user based on a set of preset worst lifestyle information (i.e., preset worst lifestyle information). The fastest aging prediction result can show the appearance changes that the target user may face under the influence of extremely poor living habits.

[0182] In some application scenarios, the fastest aging prediction result can also be referred to as a most accelerated aging prediction result, and the like, without being limited thereto.

[0183] In this implementation, the fastest aging prediction result can serve as a warning to show the user the negative consequences that an unhealthy lifestyle may bring, thereby motivating them to improve their living habits. By showing the fastest aging prediction result, the user can more clearly recognize the importance of a healthy lifestyle and the potential negative impact of poor habits on the aging process.

[0184] In one possible implementation, the fastest aging prediction result can be compared with the slowest aging prediction result to intuitively show the impact of lifestyle on the aging process.

[0185] In one possible implementation, the assumed lifestyle information includes worst information of at least one specified lifestyle item, and the aging prediction result based on the assumed condition includes an accelerated aging prediction result of the target user, which represents an aging prediction result corresponding to the target user deteriorating the at least one specified lifestyle item to the worst. The aging prediction of the target user based on the assumed condition includes aging prediction of the target user based on the face image and the assumed lifestyle information, and the aging prediction of the target user based on the assumed condition includes aging prediction of the target user based on the face image, the worst information of the at least one specified lifestyle item, and information of non-specified lifestyle items in the actual lifestyle information.

[0186] In this implementation, the specified lifestyle items can represent lifestyle components that are particularly considered in the face aging prediction analysis, such as eating habits, exercise frequency, sleep quality, smoking, and drinking, etc. For each specified lifestyle item, a worst state or standard (i.e. worst information) can be set, for example, unhealthy eating habits (such as high sugar, high fat diet), lack of exercise or physical activity, long-term sleep deficiency or poor quality, high-pressure work environment or continuous mental stress, smoking or excessive drinking, etc.

[0187] In this implementation, the target user can be predicted for aging based on the face image of the target user, the worst information of the at least one specified lifestyle item, and the information of the non-specified lifestyle items in the actual lifestyle information, to obtain an accelerated aging prediction result. In this implementation, only part of the lifestyle information is changed to the worst, and the other items still use the actual lifestyle information.

[0188] The accelerated aging prediction result can represent the predicted appearance change result of the target user's facial aging based on the face image of the target user and the worst information of the at least one specified lifestyle item. The accelerated aging prediction result shows the accelerated aging situation that the user may face if the user continues these bad habits.

[0189] In this implementation, by showing the accelerated aging result that may be caused by an unhealthy lifestyle, the user's understanding of a healthy lifestyle can be improved, and the user can be encouraged to improve bad living habits to delay the aging process.

[0190] In a possible implementation, the aging prediction result based on the actual condition includes an aging prediction image based on the actual condition, and the aging prediction result based on the hypothetical condition includes an aging prediction image based on the hypothetical condition; after obtaining the aging prediction result based on the actual condition and the aging prediction result based on the hypothetical condition, the method further includes: displaying the aging prediction image based on the actual condition and the aging prediction image based on the hypothetical condition.

[0191] In this implementation, the aging prediction result based on the actual condition can include at least one aging prediction image based on the actual condition, and the aging prediction result based on the hypothetical condition can include at least one aging prediction image based on the hypothetical condition. For example, the aging prediction result based on the actual condition can include multiple aging prediction images based on the actual condition, and the aging prediction result based on the hypothetical condition can include multiple aging prediction images based on the hypothetical condition.

[0192] In this implementation, after obtaining the aging prediction image based on the actual condition and the aging prediction image based on the hypothetical condition, the aging prediction image based on the actual condition and the aging prediction image based on the hypothetical condition can be displayed.

[0193] In a possible implementation, the aging prediction image based on the actual condition includes aging prediction images of different ages based on the actual condition, and the aging prediction image based on the hypothetical condition includes aging prediction images of different ages based on the hypothetical condition.

[0194] In this implementation, the aging prediction image based on the actual condition can include aging prediction images of different ages of the target user based on the actual condition. The aging prediction image based on the actual condition covers multiple age stages, which can be continuous or a display of key age points, so as to observe the changes of the face of the target user over time. Through the aging prediction images of different ages based on the actual condition, the aging process of the face of the target user, including the changes of skin texture, wrinkles, facial contour, etc., can be intuitively seen. FIG. 3 shows a schematic diagram of the aging prediction images of different ages of the target user based on the actual condition in the face aging prediction method provided by the embodiments of the present disclosure. In FIG. 3, the aging prediction images of the target user based on the actual condition include aging prediction images of the target user after 20 years, 30 years and 40 years based on the actual condition.

[0195] In this implementation, the aging prediction image based on the hypothetical condition can include aging prediction images of different ages based on the hypothetical condition. The aging prediction image based on the hypothetical condition can be a set of images arranged in chronological order, and each aging prediction image based on the hypothetical condition can represent the facial state of the target user at a certain age based on the hypothetical life information. The aging prediction images of different ages based on the hypothetical condition can provide a visual reference for the user, helping them understand the aging state that can be reached under the influence of different lifestyles. By comparing with the aging prediction image based on the actual condition of the target user, the aging prediction image based on the hypothetical condition can show the potential impact of lifestyle changes on the aging process.

[0196] FIG. 4 shows a schematic diagram of the fastest aging prediction result corresponding to the target user in the face aging prediction method provided by the embodiments of the present disclosure.

[0197] In a possible implementation, the aging prediction image based on the actual condition includes an aging prediction three-dimensional image based on the actual condition, and the aging prediction image based on the hypothetical condition includes an aging prediction three-dimensional image based on the hypothetical condition.

[0198] In the implementation, in terms of result display, the aging prediction result of the user at different ages can be intuitively displayed by using three-dimensional images, and a clearer visual experience can be provided for the user.

[0199] In another possible implementation, the aging prediction image based on the actual condition includes an aging prediction two-dimensional image based on the actual condition, and the aging prediction image based on the hypothetical condition includes an aging prediction two-dimensional image based on the hypothetical condition.

[0200] In a possible implementation, the aging prediction result based on the actual condition can include an aging prediction video based on the actual condition, and the aging prediction result based on the hypothetical condition can include an aging prediction video based on the hypothetical condition. In this implementation, a dynamic change process can be displayed by using the aging prediction video based on the actual condition and the aging prediction video based on the hypothetical condition, that is, the gradual change from the young face to the old face of the target user can be displayed by using the aging prediction video based on the actual condition and the aging prediction video based on the hypothetical condition.

[0201] In a possible implementation, the method further includes displaying a time axis corresponding to the aging prediction age range, and in response to any time point in the time axis being selected, displaying the aging prediction image based on the actual condition and the aging prediction image based on the hypothetical condition corresponding to the age corresponding to the selected time point.

[0202] For example, the aging prediction age range can be from 20 years old to 80 years old.

[0203] In this implementation, a time axis can be displayed to represent a range from the current age of the target user to a predicted end age (for example, from 20 years old to 80 years old). The user (for example, the target user or a researcher) can select a specific age point by clicking or operating any point on the time axis. The selection can be the current age of the user or a future age point that the user is interested in. Once the user selects a time point on the time axis, the system can display the aging prediction image (which can include the aging prediction image based on the actual condition and the aging prediction image based on the hypothetical condition) corresponding to the specific age. The user can move forward and backward along the time axis to observe the aging prediction image corresponding to different age points, thereby obtaining an intuitive understanding of the facial aging process of the target user.

[0204] FIG. 5 shows a schematic diagram of a time axis corresponding to an aging prediction age range in a face aging prediction method provided by an embodiment of the present disclosure.

[0205] In a possible implementation, the method further includes: in response to a transformation operation on any aging prediction image, performing transformation processing on the aging prediction image; wherein the aging prediction image is based on an actual condition or a hypothetical condition; and the transformation operation includes at least one of the following: a zoom-in operation, a zoom-out operation, and a rotation operation.

[0206] In this implementation, the transformation operation can represent an interactive image processing action performed on the aging prediction image. The transformation operation can allow the user to view and analyze the image in different ways.

[0207] As an example of this implementation, the user can increase the display size of the aging prediction image through the zoom-in operation, so as to observe specific details of the face of the target user more closely, such as skin texture, wrinkle depth and distribution, etc.

[0208] As an example of this implementation, the user can reduce the display size of the aging prediction image through the zoom-out operation, so as to enable the user to obtain a more macroscopic perspective and quickly grasp the overall facial features or compare the aging changes of different areas from the whole.

[0209] As an example of this implementation, the user can perform a rotation operation on the aging prediction image. The rotation operation enables the user to rotate the aging prediction image around one or more axes (such as X-axis, Y-axis, Z-axis), so as to observe the morphological changes of the face from different angles, such as observing the changes of the side profile or the three-dimensional morphology of the facial structure.

[0210] In this implementation, the transformation operation can be realized through controls in the graphical user interface (GUI), such as a slider bar, a button, or a touch gesture, ensuring that the user can easily interact with the image.

[0211] These transformation operations provide an intuitive and flexible way to explore and understand the aging prediction image, enabling the user to make personalized observations and analyses according to needs and points of interest. Through these transformation operations, the user can have a more comprehensive understanding of the aging prediction results.

[0212] In a possible implementation, the transforming the aging prediction image in response to the transforming operation on any aging prediction image comprises: transforming the aging prediction image under actual conditions and the aging prediction image under hypothetical conditions corresponding to the same age of the aging prediction image under actual conditions synchronously in response to the transforming operation on any aging prediction image under actual conditions; or transforming the aging prediction image under hypothetical conditions and the aging prediction image under actual conditions corresponding to the same age of the aging prediction image under hypothetical conditions synchronously in response to the transforming operation on any aging prediction image under hypothetical conditions.

[0213] In this implementation, if a user performs a transforming operation on an aging prediction image under actual conditions, the same transforming operation can be automatically performed on the aging prediction image under actual conditions and the aging prediction image under hypothetical conditions corresponding to the same age; if a user performs a transforming operation on an aging prediction image under hypothetical conditions, the same transforming operation can be automatically performed on the aging prediction image under hypothetical conditions and the aging prediction image under actual conditions corresponding to the same age.

[0214] This implementation provides an intuitive and personalized way to show and compare the differences between the aging prediction results under actual conditions and the aging prediction results under hypothetical conditions by synchronously transforming the images of user interactions, enhances the user's understanding of the aging process, and may encourage the user to make healthier lifestyle choices.

[0215] In a possible implementation, the aging prediction image corresponding to any age of the target user comprises a face concave-convex prediction image of the target user at the age, wherein the face concave-convex prediction image is used to show the concave-convex degree of different regions of the face.

[0216] The aging prediction image can be an aging prediction image under actual conditions or an aging prediction image under hypothetical conditions.

[0217] In this implementation, the face concave-convex prediction image can show the concave-convex degree of each region of the face in detail, which helps the user to have a more comprehensive understanding of the changes that may be caused by the aging of the face of the target user. The concave-convex degree can refer to the height difference of some regions of the face relative to other regions, which may become more obvious or change as the age increases.

[0218] In this implementation, the face relief prediction image can provide the target user with possible facial changes at different age stages in the future, including skin sagging, wrinkle formation and other aging characteristics, thereby helping the target user make more scientific and personalized health and beauty decisions.

[0219] FIG. 6 shows a schematic diagram of a face relief prediction image in a face aging prediction method according to an embodiment of the present disclosure. As shown in FIG. 6, the relief degree of different regions of the face can be shown by color or vector.

[0220] FIG. 7 shows another schematic diagram of a face relief prediction image in a face aging prediction method according to an embodiment of the present disclosure. In FIG. 7, the face relief prediction images of the target user at 20 years later, 30 years later and 40 years later under actual conditions are shown.

[0221] In one possible implementation, the method further comprises: for the same age, highlighting the facial regions in the aging prediction image under the actual condition and the aging prediction image under the hypothetical condition that have differences.

[0222] In this implementation, in the aging prediction image under the actual condition and the aging prediction image under the hypothetical condition, images of the same age can be selected for comparison. For example, the image at the age of 40 in the aging prediction image under the actual condition and the image at the age of 40 in the aging prediction image under the hypothetical condition are selected for comparison.

[0223] As an example of this implementation, computer vision techniques can be used to extract facial features such as wrinkles, skin texture, facial contours, etc. from the two aging prediction images. By comparing the features of the two aging prediction images, the differences between them can be identified. For example, it can include the number and depth of wrinkles, the degree of skin sagging, changes in facial contours, etc.

[0224] As an example of this implementation, the facial regions that have differences in the two aging prediction images can be highlighted on the image. This can be achieved by changing the color of these regions, adding a border or using different transparency.

[0225] As an example of this implementation, visual markers such as arrows, icons or annotations can be added to the facial regions that have differences in the two aging prediction images to further guide the user's attention.

[0226] As an example of this implementation, an interactive user interface can be provided to allow the user to view detailed information of specific difference regions by clicking or hovering.

[0227] As an example of this implementation, animations and transition effects can be used to show the change from one state to another in the two aging prediction images, enhancing the visual effect.

[0228] As an example of this implementation, views at different angles can be provided, allowing the user to observe the differences in the facial region from multiple directions.

[0229] As an example of this implementation, in addition to visual highlighting, quantitative data such as numerical differences in wrinkle depth, percentage changes in skin aging, etc. can also be provided.

[0230] For example, if the target user has more wrinkles in the aging prediction image based on actual conditions than in the aging prediction image based on hypothetical conditions, these wrinkles can be highlighted in red and labeled "wrinkle depth increased" next to them. For another example, if there are differences in facial contours, different colors or lines can be used to distinguish the contour differences in the two aging prediction images, and the contour changes over time can be shown through animations.

[0231] In this implementation, by highlighting the facial regions that differ between the aging prediction image based on actual conditions and the aging prediction image based on hypothetical conditions for the same age, the user can intuitively see the possible impact of different lifestyle choices on the aging process, thereby being more motivated to improve their own lifestyle.

[0232] FIG. 8 shows a schematic diagram of the slowest aging prediction results 20 years later, 30 years later and 40 years later based on the best hypothetical lifestyle information, and the fastest aging results 20 years later, 30 years later and 40 years later based on the worst hypothetical lifestyle information, in the face aging prediction method provided by the embodiments of the present disclosure.

[0233] In a possible implementation, after obtaining the aging prediction result of the target user based on actual conditions, the method further comprises: generating recommended lifestyle information corresponding to the target user according to the aging prediction result based on actual conditions and the actual lifestyle information; and outputting the recommended lifestyle information.

[0234] In this implementation, personalized lifestyle recommendations (i.e., recommended lifestyle information) can be generated in combination with the aging prediction result based on actual conditions and the actual lifestyle information. The recommended lifestyle information aims to help the user improve their aging prediction result and promote health and delay aging. The recommended lifestyle information can be output to the user in an easily understandable format, such as a text report, a chart, a graphical interface, etc.

[0235] For example, after facial aging prediction, the result shows that if he continues his current lifestyle, he may have more obvious wrinkles and skin sagging in the future. By analyzing the actual life information, it is found that the user often stays up late, has an unbalanced diet, and lacks exercise. Based on this information, recommendations for improving sleep, balanced diet, regular exercise, stress management, and other aspects of lifestyle information can be generated. For example, in the recommended lifestyle information, the user can be advised to maintain 7-8 hours of high-quality sleep every night, recommended to increase the intake of vegetables and fruits, reduce greasy and high-sugar foods, encourage the user to engage in at least 150 minutes of moderate-intensity exercise per week, and provide stress relief techniques such as meditation, yoga, or other relaxation activities.

[0236] In this implementation, by generating the recommended lifestyle information corresponding to the target user according to the aging prediction result under actual conditions and the actual life information, and outputting the recommended lifestyle information, personalized health management recommendations are provided for the user, helping them to adjust their lifestyle according to their specific circumstances. By improving their lifestyle, users can effectively delay the aging process and maintain a younger appearance and better health status.

[0237] In one possible implementation, after obtaining the aging prediction result of the target user under actual conditions, the method further includes: generating recommended cosmetic information corresponding to the target user according to the aging prediction result under actual conditions; outputting the recommended cosmetic information.

[0238] In this implementation, the aging prediction result under actual conditions can be analyzed to identify key aging characteristics such as wrinkles, skin sagging, pigmentation, etc. Based on the aging characteristics obtained from the analysis, a cosmetic recommendation algorithm can be used to recommend cosmetics suitable for the target user's current skin condition and aging characteristics. These recommendations may include anti-wrinkle creams, firming serums, whitening products, etc. In this implementation, the generated cosmetic recommendations can be output in a user-friendly manner, such as a list, chart, or graphical interface, providing information such as product name, ingredients, expected effect, usage suggestions, etc.

[0239] For example, after facial aging prediction, the result shows that her face has mild wrinkles and dry skin problems. Based on these characteristics, the following recommended cosmetic information can be generated: Anti-wrinkle cream: Recommend anti-wrinkle cream containing hyaluronic acid and peptide ingredients to increase skin elasticity and reduce fine lines; Moisturizing cream: Recommend using moisturizing cream rich in natural oils and moisturizing factors to relieve dry skin; Sunscreen: Suggest using high-SPF sunscreen to prevent UV-induced skin damage and accelerate aging; Antioxidant serum: Recommend antioxidant serum containing vitamin C and E to neutralize free radicals and protect the skin from environmental damage.

[0240] In this implementation, the recommended cosmetic information corresponding to the target user is generated according to the aging prediction result under the actual condition, and the recommended cosmetic information is output, so as to provide personalized cosmetic recommendation for the user, which is more suitable for the specific skin needs and aging characteristics of the user.

[0241] It can be understood that the above-mentioned various method embodiments mentioned in the present disclosure can be combined with each other to form combined embodiments without deviating from the principle logic. Due to the limited space, the present disclosure will not be repeated. It can be understood by those skilled in the art that the specific execution order of each step in the above-mentioned method should be determined according to its function and possible internal logic.

[0242] In addition, the present disclosure also provides a face aging prediction device, an electronic device, a computer readable storage medium, and a computer program product, which can be used to implement any one of the face aging prediction methods provided by the present disclosure. The corresponding technical solutions and technical effects can be referred to the corresponding description in the method part, and will not be repeated.

[0243] FIG. 9 shows a block diagram of a face aging prediction device according to an embodiment of the present disclosure. As shown in FIG. 9, the face aging prediction device includes:

[0244] The first obtaining module 21 is configured to obtain a face image of a target user, actual life information of the target user, and assumed life information of the target user.

[0245] The first aging prediction module 22 is configured to perform aging prediction on the target user according to the face image and the actual life information, to obtain an aging prediction result of the target user under an actual condition.

[0246] The second aging prediction module 23 is configured to perform aging prediction on the target user according to the face image and the assumed life information, to obtain an aging prediction result of the target user under a hypothetical condition.

[0247] In a possible implementation, the aging prediction result under the actual condition includes an aging prediction image under the actual condition, and the aging prediction result under the hypothetical condition includes an aging prediction image under the hypothetical condition.

[0248] The device further includes:

[0249] The first display module is configured to display the aging prediction image under the actual condition and the aging prediction image under the hypothetical condition.

[0250] In a possible implementation, the aging prediction image under the actual condition includes different aging prediction images under different ages based on the actual condition, and the aging prediction image under the assumed condition includes different aging prediction images under different ages based on the assumed condition.

[0251] In a possible implementation, the aging prediction image under the actual condition includes an aging prediction three-dimensional image under the actual condition, and the aging prediction image under the assumed condition includes an aging prediction three-dimensional image under the assumed condition.

[0252] In a possible implementation, the face image includes a three-dimensional face image of the target user.

[0253] The first obtaining module 21 is configured to: obtain a two-dimensional face image of a target user; and generate a three-dimensional face image of the target user according to the two-dimensional face image.

[0254] In a possible implementation, the assumed life information includes assumed optimal life information, and the aging prediction result under the assumed condition includes a slowest aging prediction result corresponding to the target user.

[0255] The second aging prediction module 23 is configured to:

[0256] aging prediction of the target user according to the face image and the assumed optimal life information, to obtain the slowest aging prediction result.

[0257] In a possible implementation, the assumed life information includes optimal information of at least one specified lifestyle item, and the aging prediction result under the assumed condition includes an improved aging prediction result corresponding to the target user, the improved aging prediction result indicating an aging prediction result corresponding to the target user after the target user improves the at least one specified lifestyle item to be optimal.

[0258] The second aging prediction module 23 is configured to:

[0259] aging prediction of the target user according to the face image, the optimal information of the at least one specified lifestyle item, and information of non-specified lifestyle items in the actual life information, to obtain the improved aging prediction result.

[0260] In a possible implementation, the assumed life information includes assumed worst life information, and the aging prediction result under the assumed condition includes a fastest aging prediction result corresponding to the target user.

[0261] The second aging prediction module 23 is configured to:

[0262] According to the face image and the assumed worst life information, the aging prediction of the target user is performed to obtain the fastest aging prediction result.

[0263] In a possible implementation, the assumed life information includes worst information of at least one specified lifestyle item, and the aging prediction result under the assumed condition includes an accelerated aging prediction result corresponding to the target user, which represents an aging prediction result corresponding to the target user after the target user deteriorates the at least one specified lifestyle item to the worst;

[0264] The second aging prediction module 23 is configured to:

[0265] According to the face image, the worst information of the at least one specified lifestyle item, and the information of the non-specified lifestyle item in the actual life information, the aging prediction of the target user is performed to obtain the accelerated aging prediction result.

[0266] In a possible implementation, the device further includes:

[0267] The highlighting module is configured to highlight the face regions that are different between the aging prediction image under the actual condition and the aging prediction image under the assumed condition for the same age.

[0268] In a possible implementation, the actual life information includes at least part of the following: actual life habit information, actual health status information, actual work status information, and actual living environment information.

[0269] The assumed life information includes at least part of the following: assumed life habit information, assumed health status information, assumed work status information, and assumed living environment information.

[0270] In a possible implementation, the device further includes:

[0271] The second display module is configured to display a time axis corresponding to an aging prediction age range.

[0272] The third display module is configured to, in response to any time point in the time axis being selected, display the aging prediction image under the actual condition and the aging prediction image under the assumed condition corresponding to the age corresponding to the selected time point.

[0273] In a possible implementation, the device further includes:

[0274] a transformation module, configured to perform a transformation operation on any of the aging prediction images;

[0275] wherein the aging prediction images are based on actual conditions or hypothetical conditions; and the transformation operation comprises at least one of the following: an enlargement operation, a reduction operation, a rotation operation.

[0276] In a possible implementation, the transformation module is configured to:

[0277] perform a transformation operation on any of the aging prediction images based on actual conditions and the aging prediction images based on hypothetical conditions corresponding to the same age in synchronization.

[0278] or,

[0279] perform a transformation operation on any of the aging prediction images based on hypothetical conditions and the aging prediction images based on actual conditions corresponding to the same age in synchronization.

[0280] In a possible implementation, the device further includes:

[0281] a second obtaining module, configured to obtain genetic information of the target user;

[0282] a correction module, configured to correct the aging prediction result based on actual conditions according to the genetic information, to obtain a corrected aging prediction result based on actual conditions of the target user.

[0283] In a possible implementation, the second obtaining module is configured to:

[0284] perform genetic analysis on a saliva sample of the target user to obtain the genetic information of the target user.

[0285] In a possible implementation, the device further includes:

[0286] a first generating module, configured to generate recommended lifestyle information corresponding to the target user according to the aging prediction result based on actual conditions and the actual lifestyle information.

[0287] a first output module, configured to output the recommended lifestyle information.

[0288] In a possible implementation, after the aging prediction result of the target user under the actual condition is obtained, the method further includes:

[0289] generating, by a second generation module, recommended cosmetic information corresponding to the target user according to the aging prediction result under the actual condition;

[0290] outputting, by a second output module, the recommended cosmetic information.

[0291] In a possible implementation, the aging prediction image corresponding to the target user at each age includes:

[0292] the face concave-convex prediction image corresponding to the target user at the age, wherein the face concave-convex prediction image is used to show the concave-convex degree of different regions of the face.

[0293] In some embodiments, the apparatus provided by the embodiments of the present disclosure has functions or includes modules that can be used to perform the methods described in the above method embodiments, and the specific implementation and technical effects can be referred to the description of the above method embodiments. For brevity, they will not be described here.

[0294] The embodiments of the present disclosure also provide a computer-readable storage medium having computer program instructions stored therein, and the computer program instructions are executed by a processor to implement the above method. The computer-readable storage medium can be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium.

[0295] The embodiments of the present disclosure also provide a computer program including computer readable codes, and when the computer readable codes are run in an electronic device, a processor in the electronic device executes the above method.

[0296] The embodiments of the present disclosure also provide a computer program product including computer readable codes, or a non-volatile computer readable storage medium carrying the computer readable codes, and when the computer readable codes are run in an electronic device, a processor in the electronic device executes the above method. In a possible implementation, the computer program product is specifically embodied as a webpage.

[0297] The embodiments of the present disclosure also provide an electronic device including one or more processors, and a memory for storing executable instructions, wherein the one or more processors are configured to invoke the executable instructions stored in the memory to execute the above method.

[0298] The electronic device can be provided as a terminal, a server or other forms of devices.

[0299] FIG. 10 shows a block diagram of an electronic device 1900 according to an embodiment of the present disclosure. For example, the electronic device 1900 can be provided as a server or a terminal. Referring to FIG. 10, the electronic device 1900 includes a processing component 1922, further including one or more processors, and a memory resource represented by a memory 1932, for storing instructions executable by the processing component 1922, such as an application program. The application program stored in the memory 1932 can include one or more than one module each corresponding to a set of instructions. In addition, the processing component 1922 is configured to execute the instructions to perform the above method.

[0300] The electronic device 1900 can further include a power supply component 1926 configured to perform power management of the electronic device 1900, a wired or wireless network interface 1950 configured to connect the electronic device 1900 to a network, and an input / output interface 1958 (I / O interface). The electronic device 1900 can operate based on an operating system stored in the memory 1932, such as Microsoft Windows Server TM , Apple's graphical user interface-based operating system (Mac OS X TM ), a multi-user multi-process computer operating system (Unix TM ), a free and open source Unix-like operating system (Linux TM ), an open source Unix-like operating system (FreeBSD TM ) or the like.

[0301] In an exemplary embodiment, a non-transitory computer readable storage medium, such as the memory 1932 including computer program instructions, is also provided, which can be executed by the processing component 1922 of the electronic device 1900 to complete the above method.

[0302] The present disclosure can be a system, a method, and / or a computer program product. The computer program product can include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present disclosure.

[0303] Computer readable storage media can be tangible storage media which can retain and store instructions for use by an instruction execution device. Computer readable storage media can be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of computer readable storage media include the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire.

[0304] Computer readable program instructions described herein can be downloaded to respective computing / processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and / or a wireless network. The network can comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and / or edge servers. A network adapter card or network interface in each computing / processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing / processing device.

[0305] Computer readable program instructions for carrying out operations of the present disclosure can be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer readable program instructions can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate array (FPGA), or programmable logic array (PLA) can execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present disclosure.

[0306] The computer readable program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0307] The computer readable program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0308] The computer readable program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus or other device to produce a computer implemented process such that the instructions which execute on the computer, other programmable data processing apparatus, or other device implement the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0309] The flow diagrams and the block diagrams in the drawings are presented to illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flow diagrams and the block diagrams can represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logic functions. In some alternative implementations, the functions noted in the blocks can occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and / or flow diagrams, and combinations thereof, can be implemented by dedicated hardware-based systems which perform the specified functions or acts, or combinations of hardware and software.

[0310] The computer program product can be embodied by a hardware, software or a combination thereof. In an optional embodiment, the computer program product is embodied by a computer storage medium, and in another optional embodiment, the computer program product is embodied by a software product, such as a software development kit (SDK) or the like.

[0311] The above description of the various embodiments is intended to be illustrative in all aspects, rather than restrictive. The same or similar features under different embodiments can be referred to by different names. For brevity, not all features of an embodiment are always mentioned in the description.

[0312] If the technical solutions of the embodiments of the present disclosure involve personal information, the product applying the technical solutions of the embodiments of the present disclosure has been explicitly informed of the personal information processing rules before processing the personal information and has obtained the personal independent consent. If the technical solutions of the embodiments of the present disclosure involve sensitive personal information, the product applying the technical solutions of the embodiments of the present disclosure has obtained the personal independent consent before processing the sensitive personal information and at the same time meets the requirement of "explicit consent". For example, at the personal information collection device such as a camera, a clear and prominent mark is set to inform that the personal information collection range has been entered and the personal information will be collected. If the person voluntarily enters the collection range, it is regarded as the consent to collect the personal information. Or on the device for processing personal information, the personal information processing rules are informed by using obvious marks / information, and the personal authorization is obtained by means of pop-up information or asking the person to upload the personal information by himself / herself. The personal information processing rules can include the personal information processor, the processing purpose of personal information, the processing method, the type of processed personal information and the like.

[0313] The above has described the embodiments of the present disclosure, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes are obvious to those skilled in the art without departing from the scope and spirit of the described embodiments. The selection of the terms used herein is intended to best explain the principles of the embodiments, practical application or improvement of the technology in the market, or to enable other ordinary skilled persons in the art to understand the embodiments disclosed herein.

Claims

1. A method for predicting facial aging, characterized in that, The method comprises: obtaining a face image of a target user, actual life information of the target user, and assumed life information of the target user; performing aging prediction on the target user according to the face image and the actual life information to obtain an aging prediction result of the target user under an actual condition; performing aging prediction on the target user according to the face image and the assumed life information to obtain an aging prediction result of the target user under an assumed condition.

2. The method of claim 1, wherein, The aging prediction result under the actual condition comprises an aging prediction image under the actual condition, and the aging prediction result under the assumed condition comprises an aging prediction image under the assumed condition. After obtaining the aging prediction result under the actual condition and the aging prediction result under the assumed condition, the method further comprises: displaying the aging prediction image under the actual condition and the aging prediction image under the assumed condition.

3. The method of claim 2, wherein, The aging prediction image under the actual condition comprises aging prediction images of different ages under the actual condition, and the aging prediction image under the assumed condition comprises aging prediction images of different ages under the assumed condition.

4. The method of claim 2, wherein, The aging prediction image under the actual condition comprises an aging prediction three-dimensional image under the actual condition, and the aging prediction image under the assumed condition comprises an aging prediction three-dimensional image under the assumed condition.

5. The method of claim 1, wherein: the face image comprises a three-dimensional face image of the target user; obtaining a face image of a target user comprises: obtaining a two-dimensional face image of the target user; and generating a three-dimensional face image of the target user according to the two-dimensional face image.

6. The method of claim 1, wherein, The assumed life information comprises optimal assumed life information, and the aging prediction result under the assumed condition comprises a slowest aging prediction result corresponding to the target user. The aging prediction on the target user according to the face image and the assumed life information to obtain the aging prediction result of the target user under the assumed condition comprises: aging prediction on the target user according to the face image and the optimal assumed life information to obtain the slowest aging prediction result.

7. The method of claim 1, wherein, The assumed life information comprises optimal information of at least one specified lifestyle item, and the aging prediction result under the assumed condition comprises an improved aging prediction result corresponding to the target user, which represents an aging prediction result corresponding to the target user after improving the at least one specified lifestyle item to optimal; The aging prediction on the target user according to the face image and the assumed life information to obtain the aging prediction result of the target user under the assumed condition comprises: aging prediction on the target user according to the face image, the optimal information of the at least one specified lifestyle item, and information of non-specified lifestyle items in the actual life information to obtain the improved aging prediction result.

8. The method of claim 1, wherein, The assumed life information includes assumed worst life information, and the aging prediction result based on the assumed condition includes a fastest aging prediction result corresponding to the target user. The aging prediction of the target user based on the face image and the assumed life information to obtain the aging prediction result of the target user based on the assumed condition includes: aging prediction of the target user based on the face image and the assumed worst life information to obtain the fastest aging prediction result.

9. The method of claim 1, wherein, The assumed life information includes worst information of at least one specified lifestyle item, and the aging prediction result based on the assumed condition includes an accelerated aging prediction result corresponding to the target user, which represents the aging prediction result corresponding to the target user after the at least one specified lifestyle item is deteriorated to the worst; The aging prediction of the target user based on the face image and the assumed life information to obtain the aging prediction result of the target user based on the assumed condition includes: aging prediction of the target user based on the face image, the worst information of the at least one specified lifestyle item, and the information of non-specified lifestyle items in the actual life information to obtain the accelerated aging prediction result.

10. The method according to any one of claims 1 to 9, characterized in that, The method further includes: For the aging prediction images based on the actual condition and the aging prediction images based on the assumed condition of the same age, the face regions with differences in the aging prediction images based on the actual condition and the aging prediction images based on the assumed condition are highlighted.

11. The method of any one of claims 1 to 9, wherein The actual life information includes at least part of the following: actual life habit information, actual health status information, actual working condition information, and actual living environment information; The assumed life information includes at least part of the following: assumed life habit information, assumed health status information, assumed working condition information, and assumed living environment information.

12. The method according to any one of claims 1 to 9, characterized in that, The method further includes: showing a time axis corresponding to an aging prediction age range; in response to any time point in the time axis being selected, showing the aging prediction images based on the actual condition and the aging prediction images based on the assumed condition corresponding to the age of the selected time point.

13. The method according to any one of claims 1 to 9, characterized in that, The method further includes: in response to a transformation operation for any aging prediction image, performing transformation processing on the aging prediction image; wherein the aging prediction image is an aging prediction image based on the actual condition or an aging prediction image based on the assumed condition; the transformation operation includes at least one of the following: magnification operation, reduction operation, and rotation operation.

14. The method of claim 13, wherein, The response to the transformation operation for any aging prediction image includes: in response to a transformation operation for any aging prediction image based on the actual condition, performing transformation processing on the aging prediction image based on the actual condition and the aging prediction image based on the assumed condition corresponding to the same age as the aging prediction image based on the actual condition; or In response to the transformation operation on any of the aging prediction images under the hypothetical condition, the aging prediction images under the hypothetical condition and the aging prediction images under the actual condition corresponding to the same age of the aging prediction images under the hypothetical condition are synchronously transformed.

15. The method according to any one of claims 1 to 9, characterized in that, The method further comprises: obtaining genetic information of the target user; correcting the aging prediction result under the actual condition according to the genetic information, to obtain a corrected aging prediction result under the actual condition of the target user.

16. The method of claim 15, wherein, The obtaining of the genetic information of the target user comprises: performing genetic analysis on a saliva sample of the target user to obtain the genetic information of the target user.

17. The method according to any one of claims 1 to 9, characterized in that, After the aging prediction result under the actual condition of the target user is obtained, the method further comprises: generating recommended lifestyle information corresponding to the target user according to the aging prediction result under the actual condition and the actual life information; outputting the recommended lifestyle information.

18. The method according to any one of claims 1 to 9, characterized in that, After the aging prediction result under the actual condition of the target user is obtained, the method further comprises: generating recommended cosmetic information corresponding to the target user according to the aging prediction result under the actual condition; outputting the recommended cosmetic information.

19. The method of claim 1, wherein, The aging prediction image corresponding to any age of the target user comprises: a face concave-convex prediction image corresponding to the age of the target user, wherein the face concave-convex prediction image is used to show the concave-convex degree of different regions of the face.

20. A human face aging prediction apparatus, comprising: comprise: a first obtaining module configured to obtain a face image of a target user, actual life information of the target user, and hypothetical life information; a first aging prediction module configured to perform aging prediction on the target user according to the face image and the actual life information, to obtain an aging prediction result under an actual condition of the target user; a second aging prediction module configured to perform aging prediction on the target user according to the face image and the hypothetical life information, to obtain an aging prediction result under a hypothetical condition of the target user.

21. An electronic device, comprising: comprise: one or more processors; a memory for storing executable instructions; wherein the one or more processors are configured to invoke the executable instructions stored in the memory to execute the method of any one of claims 1 to 19.

22. A computer-readable storage medium having stored thereon computer program instructions, wherein, The computer program instructions, when executed by a processor, implement the method of any one of claims 1 to 19.

23. A computer program product comprising computer readable code, or a non-transitory computer readable storage medium having computer readable code embodied thereon, the computer readable code comprising: code for receiving a request for a service from a user equipment (UE) in a wireless communication system; code for determining whether the UE is authorized to use the service; and code for providing the service to the UE if the UE is authorized to use the service. When the computer readable code runs in an electronic device, a processor in the electronic device executes the method of any one of claims 1 to 19.

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