Advertisement pushing method and system based on age and gender recognition

Abstract

The invention discloses an advertisement pushing method and system based on age and gender recognition, and relates to the technical field of intelligent advertisement pushing, and the method comprises the steps: carrying out the polymorphic fusion of extracted user features through a multi-modal fusion network, and comprehensively analyzing the age and gender information of a user; according to the age and gender information of the user, identifying an advertisement material which is most matched with the age and gender of the user from an advertisement library; based on the selected advertisement materials, generating personalized advertisement copywriting by using NLP, and generating personalized advertisement pictures and videos by using GANs; and combining the personalized advertisement copywriting with the personalized advertisement picture and video to generate personalized advertisement content. Through multi-modal data acquisition and fusion, the age and gender identification precision is improved, the advertisement content is accurately matched, personalized copywriting and vision are generated, and the advertisement attraction and the user experience are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent advertisement push technology, and in particular to an advertisement push method and system based on age and gender recognition. Background Art

[0002] In recent years, with the rapid development of big data and artificial intelligence technologies, personalized advertising has become an important means of improving advertising effectiveness and user experience. Traditional advertising systems rely primarily on basic user information and historical behavioral data, using simple statistical analysis to achieve targeted advertising. However, with the diversification of user needs and the enrichment of advertising content, traditional single-modal data processing methods are no longer able to meet the needs of precise advertising. In this context, multimodal information fusion technology has gradually become a research hotspot. By integrating multiple data sources such as facial images, voice signals, and gait information, it can more comprehensively analyze user characteristics, thereby improving the accuracy and personalization of advertising.

[0003] While there are already some systems on the market that recommend ads based on user behavior and preferences, these systems generally suffer from the following shortcomings: First, most rely on single-modality data (such as a user's browsing history or shopping behavior), ignoring other potentially useful sources of information, which limits the accuracy of ad push. Second, existing age and gender recognition technologies are mostly based on facial recognition, which is easily affected by lighting conditions, obstructions, and individual differences, resulting in low recognition accuracy and difficulty meeting the needs of targeted ad push. Furthermore, existing ad push systems lack effective feedback mechanisms, making it impossible to adjust ad content and push strategies based on real-time user feedback, resulting in poor user experience and low ad conversion rates. Summary of the Invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides an advertisement push method and system based on age and gender recognition to solve the problems of insufficient multimodal information fusion, inaccurate age and gender recognition, and low advertisement push accuracy.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, an embodiment of the present invention provides an advertising push method based on age and gender recognition, which includes collecting user information and extracting features from the collected user information; using a multimodal fusion network to perform polymorphic fusion on the extracted user features, and comprehensively analyzing the user's age and gender information; based on the user's age and gender information, identifying advertising materials that best match the user's age and gender from an advertising library; based on the selected advertising materials, using NLP to generate personalized advertising copy, and using GANs to generate personalized advertising images and videos; combining the personalized advertising copy with personalized advertising images and videos to generate personalized advertising content; pushing the personalized advertising content to users, and collecting user feedback in real time, and optimizing the advertising content and push strategy based on the user's real-time feedback.

[0008] As a preferred solution of the method for pushing advertisements based on age and gender recognition according to the present invention, the steps of collecting user information and extracting features from the collected user information are as follows:

[0009] The system captures the user's facial image through high-resolution cameras installed in public places, records the user's voice through an integrated microphone array, and uses thermal imagers and infrared sensors to detect the user's gait information;

[0010] Use facial detection algorithms to detect facial regions from images and extract facial features, including eyes, nose, and mouth features;

[0011] Perform noise reduction on the audio signal, perform spectrum analysis using FFT, and extract speech features, including fundamental frequency and formant frequency;

[0012] The gait information is filtered and smoothed, and a Kalman filter is used to remove noise and extract gait features, including step length, stride length, and speed.

[0013] As a preferred solution of the advertising push method based on age and gender recognition of the present invention, wherein: the multimodal fusion network is used to perform multimodal fusion on the extracted user features and comprehensively analyze the user's age and gender information. The specific steps are as follows:

[0014] The extracted facial features, language features, and gait features are fused using a multimodal fusion network. The expression is:

[0015] F=[F f ; F v ; F g ];

[0016] Among them, F is the comprehensive feature vector, F f is the facial feature vector, F v is the speech feature vector, F gis the gait feature vector;

[0017] Based on the fused comprehensive feature vector F, the comprehensive perception model MLP is used to comprehensively analyze the user's age and gender information;

[0018] User age analysis, the expression is:

[0019]

[0020] Among them, A is the predicted value of user age, F i is the i-th feature in the comprehensive feature vector F, α i is the amplitude coefficient of the i-th feature, d j is the bias term of the jth layer, m is the number of hidden layers, n is the dimension of the comprehensive feature vector F, σ is the activation function, e is the global bias term, tanh is the nonlinear activation function, and Y i is the phase shift of the i-th eigenvector;

[0021] User gender analysis, the expression is:

[0022]

[0023] Among them, β i is the frequency coefficient of the i-th feature, G represents the gender bias rate;

[0024] Based on the comprehensive feature vector F, define a gender discrimination threshold T G , determine the user's gender;

[0025] When G>T G , then it is determined to be female;

[0026] When G<T G , it is determined to be male.

[0027] As a preferred solution of the method for pushing advertisements based on age and gender identification of the present invention, the steps of identifying the advertisement material that best matches the user's age and gender from the advertisement library according to the user's age and gender information are as follows:

[0028] Based on product information and creative content, define an ad library containing multiple ad creatives, each with an associated target age range and target gender skew;

[0029] Based on the user's age and gender, a matching function M is defined to identify the matching degree between each ad creative and the user's age and gender information. The expression is:

[0030]

[0031] Among them, q k represents the kth creative, μ k Indicates creative q k The average of the target age range, & k Indicates creative q k Standard deviation of the target age range, ρ k Indicates creative q k The central value of the target gender bias rate, T k Indicates creative q k The standard deviation of the target gender bias rate, M(q k , A, G) is a measure of the advertising creative q k The degree of match with the user's age prediction value A and gender bias rate G;

[0032] Based on business needs and historical data, define a matching standard threshold T M , identify highly matching advertising creatives;

[0033] When M(q k , A, G)≥T M When the current ad creative is considered to be a high-matching creative;

[0034] When M(q k , A, G)<T M , the current creative is considered not to be a high-match creative.

[0035] As a preferred solution of the advertising push method based on age and gender recognition described in the present invention, wherein: based on the selected advertising material, NLP is used to generate personalized advertising copy, and GANs is used to generate personalized advertising pictures and videos. The specific steps are as follows:

[0036] Extract key information from the selected advertising materials, including product name, features, and target audience, to form a set of keywords K;

[0037] Generate a copy template based on the type of advertising material and the target audience, fill the relevant information in the keyword set K into the placeholder position in the copy template, and generate preliminary personalized advertising copy;

[0038] Use NLP to polish the generated preliminary copy, including synonym replacement and sentence structure adjustment, to generate personalized advertising copy;

[0039] Based on the content and style of the selected creatives, collect relevant reference images and video clips to train the GANs model;

[0040] Use the trained GANs model to generate personalized images and videos that are consistent with the style of the ad creative.

[0041] As a preferred solution of the advertising push method based on age and gender recognition of the present invention, the personalized advertising copy is combined with personalized advertising pictures and videos to generate personalized advertising content. The specific steps are as follows:

[0042] By coordinating the consistency between the copy and the image content, the generated personalized advertising copy is matched with the personalized advertising image to enhance the visual appeal;

[0043] By embedding the generated personalized advertising copy into personalized videos and presenting the copy content in the form of subtitles and narration, the video's appeal is enhanced.

[0044] Once you've finalized your composition, use Adobe Premiere Pro to export your final personalized ad content.

[0045] As a preferred solution of the advertising push method based on age and gender recognition described in the present invention, the personalized advertising content is pushed to the user, and user feedback is collected in real time. The advertising content and push strategy are optimized according to the real-time feedback of the user. The specific steps are as follows:

[0046] Based on the user's active time period, personalized advertising content is generated and pushed to target users through social media;

[0047] Through the built-in feedback collection mechanism, users' reactions to ads are collected in real time, including click-through rate, dwell time, and number of shares. Users' subjective feedback is directly obtained through questionnaires and online reviews.

[0048] Optimize push timing, frequency, and ad content based on user feedback to increase effective ad reach;

[0049] For users with positive feedback, we provide more personalized advertising content. For users with negative feedback, we optimize the push strategy to reduce interference with users' lives.

[0050] In a second aspect, the present invention provides an advertising push system based on age and gender recognition, including a data collection and feature extraction module, a multimodal fusion and analysis module, an advertising material matching module, an advertising content generation module based on selected advertising materials, and an advertising push and feedback module; the data collection and feature extraction module is used to collect user information and perform feature extraction on the collected user information; the multimodal fusion and analysis module is used to use a multimodal fusion network to perform polymorphic fusion on the extracted user features and comprehensively analyze the user's age and gender information; the advertising material matching module is used to identify the advertising material that best matches the user's age and gender from the advertising library based on the user's age and gender information; based on the selected advertising material: used to use NLP to generate personalized advertising copy, and use GANs to generate personalized advertising pictures and videos; the advertising content generation module is used to combine personalized advertising copy with personalized advertising pictures and videos to generate personalized advertising content; the advertising push and feedback module is used to push personalized advertising content to users, collect user feedback in real time, and optimize advertising content and push strategies based on users' real-time feedback.

[0051] In a third aspect, an embodiment of the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, any step of the advertising push method based on age and gender recognition as described in the first aspect of the present invention is implemented.

[0052] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the advertising push method based on age and gender recognition as described in the first aspect of the present invention.

[0053] The beneficial effects of the present invention are as follows: through multimodal data collection and feature extraction, the present invention realizes the comprehensive acquisition of user characteristics from facial images, voice signals and gait information, thereby enhancing the diversity and accuracy of data; through multimodal fusion and comprehensive analysis, it effectively integrates multi-source heterogeneous data and improves the accuracy of age and gender recognition; through the selection of highly matching advertising materials, it accurately screens out advertising content that best matches user characteristics, thereby improving the relevance and effectiveness of advertising; through personalized advertising content generation, NLP and GANs technology are used to generate highly customized copy and visual content, thereby enhancing the attractiveness and interactivity of advertising, and ultimately achieving the precision and personalization of advertising push, greatly improving user experience and advertising conversion rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0055] Figure 1 This is a flowchart of the advertising push method based on age and gender recognition in Example 1.

[0056] Figure 2 This is a flowchart of the advertising push system based on age and gender recognition in Example 1. DETAILED DESCRIPTION

[0057] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0058] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0059] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0060] Example 1, with reference to Figure 1 and Figure 2 , which is the first embodiment of the present invention, provides an advertisement push method based on age and gender recognition, comprising the following steps:

[0061] S1. Collect user information and perform feature extraction on the collected user information.

[0062] Going a step further, high-resolution cameras installed in public places are used to capture users’ facial images, integrated microphone arrays are used to record users’ voices, and thermal imagers and infrared sensors are used to detect users’ gait information;

[0063] A facial detection algorithm is used to detect facial regions from images and extract facial features, including those of the eyes, nose, and mouth. For example, after a high-resolution camera captures a user's facial image, the system uses a facial detection algorithm (such as the deep learning-based MTCNN) to locate the facial region and further identify the specific positions of the eyes, nose, and mouth. By analyzing the shape and position of these key features, the system can extract features such as eye size and spacing, nose shape and position, and mouth contour. This facial feature data is then used for subsequent age and gender recognition analysis.

[0064] The system performs noise reduction on the audio signal and uses FFT to perform spectral analysis to extract speech features, including fundamental frequency and formant frequencies. For example, when the integrated microphone array records the user's voice, the system first performs noise reduction on the audio signal to remove ambient noise. It then uses a Fast Fourier Transform (FFT) to convert the time-domain signal into a frequency-domain signal, extracting the fundamental frequency (the fundamental frequency of vocal cord vibration) and formant frequencies (the resonant frequencies of different vowels in the voice). These speech features help the system more accurately determine the user's age and gender.

[0065] The gait information is filtered and smoothed, and a Kalman filter is used to remove noise and extract gait features, including stride length, stride length, and speed. For example, after the thermal imager and infrared sensor detect the user's gait information, the system first filters and smoothes the raw data to eliminate noise introduced by environmental interference and sensor errors. The Kalman filter is then used to further optimize the data to improve its stability and accuracy. By analyzing the processed data, the system is able to extract the user's stride length (the length of each step), stride length (the total length of two consecutive steps), and speed (walking speed). These gait features will provide additional supporting information for age and gender recognition.

[0066] S2. Use the multimodal fusion network to perform multimodal fusion on the extracted user features and comprehensively analyze the user's age and gender information.

[0067] Furthermore, a multimodal fusion network is used to fuse the extracted facial features, language features, and gait features. The expression is:

[0068] F=[F f ; F v ; F g ];

[0069] Among them, F is the comprehensive feature vector, F f is the facial feature vector, F v is the speech feature vector, F g is the gait feature vector;

[0070] It should be noted that the system first expresses the extracted facial features (such as the shape of eyes, nose and mouth), language features (such as fundamental frequency and formant frequency) and gait features (such as step length, stride length and speed) as feature vectors Ff, F v and F g Then, a multimodal fusion network is used to fuse these three feature vectors into a comprehensive feature vector F = [F f ; F v ; F g ], the fused feature vectors are processed through a neural network multi-layer perceptron (MLP) to generate a comprehensive user feature representation for subsequent age and gender recognition analysis. This process effectively integrates multi-source heterogeneous data through a deep learning model, improving the accuracy and robustness of age and gender recognition.

[0071] Based on the fused comprehensive feature vector F, the comprehensive perception model MLP is used to comprehensively analyze the user's age and gender information;

[0072] User age analysis, the expression is:

[0073]

[0074] Among them, A is the predicted value of user age, F i is the i-th feature in the comprehensive feature vector F, α i is the amplitude coefficient of the i-th feature, d j is the bias term of the jth layer, m is the number of hidden layers, n is the dimension of the comprehensive feature vector F, σ is the activation function (Sigmoid function), e is the global bias term, tanh is the nonlinear activation function, Y i is the phase shift of the i-th eigenvector;

[0075] User gender analysis, the expression is:

[0076]

[0077] Among them, β i is the frequency coefficient of the i-th feature, G represents the gender bias rate;

[0078] Based on the comprehensive feature vector F, define a gender discrimination threshold T G , determine the user's gender;

[0079] When G>T G , then it is determined to be female;

[0080] When G<T G , it is determined to be male.

[0081] It should also be noted that the activation function σ maps the weighted input value of each node in the neural network to a nonlinear interval, allowing the model to capture complex patterns in the data. In this process, the activation function plays a role in converting the internal calculation results into a probability distribution that is more consistent with the actual probability distribution, thereby improving the accuracy and robustness of age and gender recognition. For example, in age analysis, the activation function converts the internal calculation results into a value between 0 and 1, which can be interpreted as the probability of the user's age prediction; in gender analysis, the activation function similarly converts the internal calculation results into a probability value representing the probability of the user's gender bias. In this way, the activation function helps the system better understand and identify the user's age and gender characteristics.

[0082] S3. Based on the user's age and gender information, identify the advertising creative that best matches the user's age and gender from the advertising library.

[0083] Furthermore, based on product information and creative content, an advertising library containing multiple advertising creatives is defined, and each advertising creative has an associated target age range and target gender bias rate; the specific process is: by analyzing the basic information of the product (such as product name, functional features, brand image, etc.) and creative content (such as copy, pictures and videos), the target age range and target gender bias rate of each advertising creative are determined, thereby building a systematic advertising library to ensure that each advertising creative can accurately match a specific audience group.

[0084] Based on the user's age and gender, a matching function M is defined to identify the matching degree between each ad creative and the user's age and gender information. The expression is:

[0085]

[0086] Among them, q k represents the kth creative, μ k Indicates creative q k The average of the target age range, & k Indicates creative q k Standard deviation of the target age range, ρ k Indicates creative q k The central value of the target gender bias rate, T k Indicates creative q k The standard deviation of the target gender bias rate, M(q k , A, G) is a measure of the advertising creative q k The degree of match with the user's age prediction value A and gender bias rate G;

[0087] Based on business needs and historical data, define a matching standard threshold T M, identify highly matching advertising creatives;

[0088] When M(q k , A, G)≥T M When the current ad creative is considered to be a high-matching creative;

[0089] When M(q k , A, G)<T M , the current creative is considered not to be a high-match creative.

[0090] Based on the comparison results, the system can screen out highly matched advertising materials that are most suitable for the target users, thereby improving the accuracy and effectiveness of advertising push.

[0091] S4. Based on the selected advertising materials, use NLP to generate personalized advertising copy, and use GANs to generate personalized advertising images and videos;

[0092] Furthermore, key information is extracted from the selected creatives, including the product name, features, and target audience, to form a set of keywords K. For example, from a selected creative, the system extracts the product name "smartwatch," the features "health monitoring, fitness tracking, smart notifications," and the target audience "young professionals." This key information is aggregated into a set of keywords K, including "smartwatch," "health monitoring," "fitness tracking," "smart notifications," and "young professionals." This process ensures that the extracted key information accurately reflects the core content of the creative and the characteristics of the target audience.

[0093] Generate a copy template based on the creative type and target audience. Fill the placeholders in the copy template with relevant information from the keyword set K to generate preliminary personalized ad copy. For example, a copy template containing placeholders is generated based on the creative type and target audience. Suppose the creative is about a smartwatch, and the target audience is young professionals. The keyword set K includes "smartwatch," "health monitoring," "exercise tracking," "smart notifications," and "young professionals."

[0094] NLP is used to refine the generated preliminary copy, including synonym replacement and sentence structure adjustments, to generate personalized advertising copy. For example, the slogan "Smartwatch designed for young professionals, with health monitoring, fitness tracking, and smart notifications, making your life more convenient and healthy" was refined to "Smartwatch designed for busy young professionals, with health monitoring, fitness tracking, and smart notifications, helping you easily manage your life and enjoy a healthier every day." Synonym replacement and sentence structure adjustments make the copy more vivid and engaging.

[0095] Based on the content and style of the selected creative, relevant reference images and video clips are collected to train the GANs model; the specific process is as follows: Based on the content and style of the selected creative, the system collects a series of relevant reference images and video clips, and then uses these reference materials to train the Generative Adversarial Network (GANs) model to ensure that the generated personalized images and videos are consistent with the style of the creative.

[0096] Use the trained GANs model to generate personalized images and videos that are consistent with the style of the ad creative.

[0097] It should also be noted that through the trained GANs model, corresponding personalized pictures and videos are generated according to the content and style of the advertising creatives, ensuring that the generated visual content is consistent with the style of the advertising creatives, thereby enhancing the attractiveness and relevance of the advertisements.

[0098] S5. Combine personalized advertising copy with personalized advertising images and videos to generate personalized advertising content;

[0099] Furthermore, by coordinating the consistency between the copy and the image content, the generated personalized advertising copy is matched and combined with the personalized advertising image to enhance the visual appeal;

[0100] By embedding the generated personalized advertising copy into personalized videos and presenting the copy content in the form of subtitles and narration, the video's appeal can be increased. For example, the generated personalized advertising copy "A smart watch designed for busy young professionals, with health monitoring, exercise tracking and smart notification functions, helping you easily manage your life and enjoy a healthier every day" can be displayed in the form of subtitles at key moments in the video, accompanied by corresponding narration, to ensure that the copy content is coordinated with the video screen, thereby increasing the video's appeal.

[0101] Once you've finalized your composition, use Adobe Premiere Pro to export your final personalized ad content.

[0102] It should also be noted that by coordinating the consistency of the copy and image content and embedding personalized ad copy into the video, presenting it in the form of subtitles and narration, the copy and video were coordinated, thereby enhancing the video's appeal. Ultimately, using Adobe Premiere Pro to export high-quality personalized ad content, the overall consistency and visual appeal of the ad were enhanced, improving the user experience and advertising effectiveness.

[0103] S6. Push personalized advertising content to users and collect user feedback in real time. Based on the real-time feedback from users, optimize the advertising content and push strategy.

[0104] Furthermore, personalized advertising content will be generated and pushed to target users through social media based on the user's active time period;

[0105] Through the built-in feedback collection mechanism, users' reactions to ads are collected in real time, including click-through rate, dwell time, and number of shares. Users' subjective feedback is directly obtained through questionnaires and online reviews.

[0106] Optimize push timing, frequency, and ad content based on user feedback to increase effective ad reach;

[0107] For users with positive feedback, we provide more personalized advertising content. For users with negative feedback, we optimize the push strategy to reduce interference with users' lives.

[0108] It should also be noted that a built-in feedback collection mechanism automatically tracks and records users' immediate reactions to push ads, such as objective data such as click-through rate, page dwell time, and sharing behavior, and uses questionnaires and online reviews to collect subjective user feedback. Subsequently, based on the collected feedback, the system dynamically adjusts the timing, frequency, and content of ad push notifications to improve the effective reach of ads. For users who provide positive feedback, the system further provides personalized advertising content that better suits their interests. For users who provide negative feedback, the system adjusts the push strategy, reducing the frequency or changing the push method, thereby reducing user intrusion and improving the user experience. This mechanism not only improves the user experience but also enhances the system's adaptive capabilities, enabling it to continuously improve and optimize ad push strategies, thereby improving overall advertising effectiveness and user satisfaction.

[0109] This embodiment also provides an advertising push system based on age and gender recognition, including: a data collection and feature extraction module, a multimodal fusion and analysis module, an advertising material matching module, an advertising content generation module based on selected advertising materials, and an advertising push and feedback module; the data collection and feature extraction module is used to collect user information and extract features from the collected user information; the multimodal fusion and analysis module is used to use a multimodal fusion network to perform polymorphic fusion on the extracted user features and comprehensively analyze the user's age and gender information; the advertising material matching module is used to identify the advertising material that best matches the user's age and gender from the advertising library based on the user's age and gender information; based on the selected advertising material: it is used to use NLP to generate personalized advertising copy and use GANs to generate personalized advertising images and videos; the advertising content generation module is used to combine personalized advertising copy with personalized advertising images and videos to generate personalized advertising content; the advertising push and feedback module is used to push personalized advertising content to users, collect user feedback in real time, and optimize advertising content and push strategies based on the user's real-time feedback.

[0110] This embodiment also provides a computer device suitable for the case of an advertisement push method based on age and gender identification, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement the advertisement push method based on age and gender identification proposed in the above embodiment.

[0111] The computer device may be a terminal, comprising a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device comprises a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner may be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a button, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse.

[0112] This embodiment also provides a storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for implementing advertisement push based on age and gender recognition as proposed in the above embodiment; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable red-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, disk or optical disk.

[0113] In summary, the present invention achieves comprehensive acquisition of user features from facial images, voice signals and gait information through: multimodal data collection and feature extraction, thereby enhancing data diversity and accuracy; through multimodal fusion and comprehensive analysis, effectively integrating multi-source heterogeneous data, and improving the accuracy of age and gender recognition; through the selection of highly matching advertising materials, accurately screening out advertising content that best matches user characteristics, thereby improving the relevance and effectiveness of advertising; through personalized advertising content generation, using NLP and GANs technology to generate highly customized copy and visual content, thereby enhancing the attractiveness and interactivity of advertising, and ultimately achieving precise and personalized advertising push, greatly improving user experience and advertising conversion rate.

[0114] Example 2, referring to Table 1, is the second example of the present invention. To further verify the technical solution of the present invention, experimental simulation data of an advertisement push method based on age and gender recognition is provided.

[0115] To verify the effectiveness of the present invention, this example set up several information collection points in a large shopping mall. These points were equipped with high-resolution cameras, microphone arrays, thermal imagers, and infrared sensors. These devices were used to capture users' facial images, record their voices, and detect their gait information.

[0116] First, the experiment set up information collection points in a shopping mall, using high-resolution cameras, microphone arrays, thermal imagers and infrared sensors to collect users' facial images, voices and gait information, and extracted facial, voice and gait features through algorithms.

[0117] Secondly, the extracted multimodal features are comprehensively processed through a multimodal fusion network to generate a comprehensive feature vector for accurately identifying the user's age and gender.

[0118] Next, the system selects highly matching advertising materials from the advertising library based on the user's age and gender information, and uses NLP and GANs technology to generate personalized advertising copy and pictures and videos.

[0119] Finally, personalized advertising content is pushed to users through social media platforms, and user feedback is collected in real time. The advertising push strategy is optimized based on the feedback to improve user experience and advertising effectiveness.

[0120] The details are shown in Table 1 below:

[0121] Table 1 User information and advertising effect statistics

[0122]

[0123] The above experimental data shows that the multimodal user information collection and analysis system of the present invention demonstrates significant accuracy in age prediction and gender recognition. Taking User 1 as an example, the predicted age is 28 years old, and the gender bias rate is 0.54 (close to 0.5 means that the gender bias is not obvious), which shows that the system can accurately identify the user's age and gender characteristics. Further observing the advertising material matching degree, User 1's matching degree is 0.82, which means that the system can effectively match the advertising material with the user's age and gender information.

[0124] The present invention's age- and gender-based advertising push method significantly improves not only the accuracy and effectiveness of advertising but also the user experience and satisfaction. This is particularly evident through multimodal feature fusion and personalized content generation, demonstrating the present invention's significant advantages in improving ad matching and user interaction.

[0125] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. An advertisement push method based on age and gender recognition, characterized in that: include, Collect user information and perform feature extraction on the collected user information; Use a multimodal fusion network to perform multimodal fusion on the extracted user features and comprehensively analyze the user's age and gender information; According to the user's age and gender information, identify the advertising creative that best matches the user's age and gender from the advertising library; Based on the selected ad creatives, NLP is used to generate personalized ad copy, and GANs is used to generate personalized ad images and videos. Combine personalized advertising copy with personalized advertising pictures and videos to generate personalized advertising content; Push personalized advertising content to users, collect user feedback in real time, and optimize advertising content and push strategies based on real-time user feedback.

2. The method for advertising based on age and gender recognition as claimed in claim 1, characterized in that: The user information is collected and features are extracted from the collected user information. The specific steps are as follows: The user's facial image is captured by a high-resolution camera installed in a public place, the user's voice is recorded by an integrated microphone array, and the user's gait information is detected using a thermal imager and an infrared sensor; Use facial detection algorithms to detect facial regions from images and extract facial features, including eyes, nose, and mouth features; Perform noise reduction on the audio signal, use FFT to perform spectrum analysis, and extract speech features, including fundamental frequency and formant frequency; The gait information is filtered and smoothed, and the Kalman filter is used to remove noise and extract gait features, including step length, stride length, and speed.

3. The method for advertising based on age and gender recognition as claimed in claim 2, characterized in that: The multimodal fusion network is used to perform multimodal fusion on the extracted user features and comprehensively analyze the user's age and gender information. The specific steps are as follows: The extracted facial features, language features and gait features are fused using a multimodal fusion network. The expression is: F=[F f ;F v ;F g ]; Among them, F is the comprehensive feature vector, F f is the facial feature vector, F v is the speech feature vector, F g is the gait feature vector; Based on the fused comprehensive feature vector F, the comprehensive perception model MLP is used to comprehensively analyze the user's age and gender information; User age analysis, the expression is: Among them, A is the predicted value of user age, F i is the i-th feature in the comprehensive feature vector F, a i is the amplitude coefficient of the ith feature, d j is the bias term of the jth layer, m is the number of hidden layers, n is the dimension of the comprehensive feature vector F, σ is the activation function, e is the global bias term, tanh is the nonlinear activation function, and Y i is the phase shift of the i-th eigenvector; User gender analysis, the expression is: Among them, β i is the frequency coefficient of the ith feature, and G represents the gender bias rate; Based on the comprehensive feature vector F, define a gender discrimination threshold T G , determine the user's gender; When G>T G , then it is judged as female; When G <T G , it is determined to be male.

4. The method for advertising based on age and gender recognition as claimed in claim 3, characterized in that: The specific steps of identifying the advertising material that best matches the user's age and gender from the advertising library according to the user's age and gender information are as follows: Based on the product information and creative content, define an ad library containing multiple creatives, each with a target age range and target gender skew associated with it; Based on the user's age and gender, define a matching function to identify the matching degree between each ad creative and the user's age and gender information. The expression is: Among them, q k represents the kth creative, μ k Indicates the creative q k The average of the target age range, & k Indicates the creative q k Standard deviation of the target age range, ρ k Indicates the creative q k The central value of the target gender bias ratio, T k Indicates the creative q k The standard deviation of the target gender bias rate, M(q k , A, G) is a measure of the advertising creative q k The matching degree with the user's age prediction value A and gender bias rate G; Based on business needs and historical data, define a matching standard threshold T M , identify highly matching advertising creatives; When M(q k , A, G) ≥ T M When the current ad creative is considered to be a high-matching creative; When M(q k , A, G) <T M , the current creative is considered not to be a high-matching creative.

5. The method for advertising based on age and gender recognition as claimed in claim 4, characterized in that: Based on the selected advertising material, NLP is used to generate personalized advertising copy, and GANs is used to generate personalized advertising pictures and videos. The specific steps are as follows: Extract key information from the selected advertising materials, including product name, functional features and target audience, to form a set of keywords K; Generate a copy template according to the type of advertising material and the target audience, fill the relevant information in the keyword set K into the placeholder position in the copy template, and generate preliminary personalized advertising copy; Use NLP to polish the generated preliminary copy, including synonym replacement and sentence structure adjustment, to generate personalized advertising copy; According to the content and style of the selected creatives, collect relevant reference images and video clips to train the GANs model; Use the trained GANs model to generate personalized images and videos that are consistent with the style of the ad creatives.

6. The method for advertising based on age and gender recognition as claimed in claim 5, characterized in that: The personalized advertising copy is combined with personalized advertising pictures and videos to generate personalized advertising content. The specific steps are as follows: By coordinating the consistency between the copy and the image content, the generated personalized advertising copy is matched and combined with the personalized advertising image to enhance the visual appeal; By embedding the generated personalized advertising copy into personalized videos and presenting the copy content in the form of subtitles and narration, the video's appeal can be improved; Once the composition is complete, use Adobe Premiere Pro to export the final personalized advertising content.

7. The method for advertising based on age and gender recognition as claimed in claim 6, characterized in that: The personalized advertising content is pushed to the user, and the user feedback is collected in real time. The advertising content and push strategy are optimized according to the user's real-time feedback. The specific steps are as follows: Based on the user's active time period, the generated personalized advertising content will be pushed to the target users through social media; Through the built-in feedback collection mechanism, users' responses to ads are collected in real time, including click-through rate, dwell time and number of shares, and subjective feedback from users is directly obtained through questionnaires and online evaluations; Optimize push timing, frequency and advertising content based on user feedback to increase the effective reach of advertising; For users with positive feedback, provide more personalized advertising content; for users with negative feedback, optimize push strategies to reduce interference with users' lives.

8. An advertisement push system based on age and gender recognition, based on the advertisement push method based on age and gender recognition according to any one of claims 1 to 7, characterized in that: Including data collection and feature extraction module, multimodal fusion and analysis module, advertising material matching module, advertising content generation module based on selected advertising materials, and advertising push and feedback module; The data collection and feature extraction module is used to collect user information and perform feature extraction on the collected user information; The multimodal fusion and analysis module is used to use the multimodal fusion network to perform multimodal fusion on the extracted user features and comprehensively analyze the user's age and gender information; An advertisement creative matching module, used for identifying, from an advertisement library, advertisement creatives that best match the age and gender of the user based on the age and gender information of the user; Based on the selected ad creatives: used to generate personalized ad copy using NLP, and to generate personalized ad images and videos using GANs; An advertising content generation module, used to combine personalized advertising copy with personalized advertising pictures and videos to generate personalized advertising content; The advertising push and feedback module is used to push personalized advertising content to users, collect user feedback in real time, and optimize advertising content and push strategies based on users' real-time feedback.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the advertisement push method based on age and gender recognition according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the advertisement push method based on age and gender recognition according to any one of claims 1 to 7 are implemented.

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