Intelligent method for facial recognition of depression

Abstract

The invention discloses an intelligent depression facial recognition method, and relates to the technical field of facial recognition. The method comprises the following steps: S1, sample collection: collecting a large-scale face image data set including images of healthy individuals and depression patients; the data can be obtained through clinical data, social media or a public data set; s2, face detection: using a face detection algorithm (Haar cascade, MTCNN) to identify a face region in the image, and extracting face features; and S3, emotion feature extraction: extracting facial expression features by using a deep learning model (convolutional neural network, CNN). According to the method, through sample collection, a large-scale face image data set is collected, including images of healthy individuals and depression patients, so that an operator can quickly determine the features of the samples (the healthy individuals and the depression patients), research can focus on a specific research target, and the correlation of research is improved.

Description

Technical Field

[0001] The invention belongs to the technical field of facial recognition, and in particular relates to an intelligent method for facial recognition of depression. Background Art

[0002] The application of facial recognition technology in the fields of sentiment analysis and mental health has gradually gained attention. Studies have shown that facial expressions are closely related to an individual's emotional state, especially in the identification of depression, where changes in facial expressions may reflect the patient's inner state. The following is an introduction to the background technology of intelligent methods for facial recognition of depression: Overview of facial recognition technology Principle: Facial recognition technology determines an individual's identity or emotional state by analyzing and identifying facial features (eyes, nose, mouth position and shape). Commonly used technologies include deep learning and computer vision. Algorithms: The current mainstream facial recognition algorithms include convolutional neural networks (CNN) and regional convolutional neural networks (R-CNN). These algorithms can automatically extract facial features and perform classification or sentiment analysis. Sentiment analysis and depression Emotion recognition: By analyzing facial expressions, basic emotions (happiness, sadness, anger, surprise, disgust, fear) can be identified. Patients with depression often show a reduction in facial expressions or specific emotional patterns, such as often showing sadness or loss. Facial feature extraction: Studies have shown that the facial expressions of patients with depression may lack changes, or the connection between their expressions and emotional states is weak. Through feature extraction, these changes can be analyzed and sentiment can be classified. Technological Advances Deep Learning: The application of deep learning technology in image processing has greatly improved the accuracy of facial recognition. Through training with a large amount of labeled data, the model can recognize complex facial features and emotional patterns. Real-time analysis: With the improvement of computing power, real-time facial recognition and sentiment analysis become possible. This makes it possible to monitor the emotional state of individuals in various environments (home, hospital, school). Application Scenarios Mental Health Monitoring: Facial recognition technology can be used to monitor and evaluate the emotional changes of patients with depression and provide timely mental health support. Clinical Diagnosis: In clinical settings, facial recognition technology can assist doctors in evaluating depression and help improve the accuracy of diagnosis. Ethics and Privacy Data Privacy: Facial recognition technology must comply with privacy protection regulations during use. Ensuring user consent and protecting personal data security are important prerequisites for implementing this technology. Ethical Considerations: When using facial recognition technology for mental health assessment, possible ethical issues should be fully considered. Labeling and discrimination. Summary The research on intelligent methods for facial recognition depression combines cutting-edge technologies in computer vision, sentiment analysis, and mental health, providing new ideas and tools for identifying and supporting patients with depression. The application of this technology has the potential to improve the identification rate and intervention effectiveness of depression, but it also requires attention to privacy and ethical issues. Summary of the invention

[0003] The purpose of the present invention is to provide an intelligent method for facial recognition of depression through sample collection: collecting a large-scale facial image data set, including images of healthy individuals and patients with depression so that the operator can quickly identify the characteristics of the samples (healthy individuals and patients with depression), and the research can focus on specific research objectives, improve the relevance of the research, and solve existing problems.

[0004] To solve the above technical problems, the present invention is achieved through the following technical solutions:

[0005] The present invention is an intelligent method for facial recognition of depression, comprising the following steps:

[0006] S1: Sample collection: Collect a large-scale facial image dataset, including images of healthy individuals and patients with depression. It can be obtained through clinical data, social media or public datasets;

[0007] S2: Face detection: Use face detection algorithms (Haar cascade, MTCNN) to identify the facial area in the image and extract facial features;

[0008] S3: Emotional feature extraction: Use deep learning models (convolutional neural networks, CNN) to extract facial expression features. These features can reflect emotional states;

[0009] S4: Training model: Use the labeled dataset to train the model and optimize the parameters to improve the recognition accuracy of depression;

[0010] S5: Performance evaluation: Use accuracy, recall, and F1-score indicators to evaluate the performance of the model. The confusion matrix can be used to analyze the classification results;

[0011] S6: Build an application: Develop a user-friendly application that enables users to upload facial images and obtain depression risk assessment results;

[0012] S7: Ethical review: Ensure that ethical standards are followed when conducting facial recognition and depression identification to avoid negative impacts on individuals;

[0013] S8: User tracking: Regularly monitor the user's emotional changes and provide corresponding mental health support;

[0014] S9: Model Updates: As new data is collected and technology develops, the model is updated regularly to improve recognition accuracy and reliability.

[0015] Furthermore, the S1 is further expressed as:

[0016] S1.1: Sample population definition: Determine the characteristics of the target sample, including healthy individuals and patients with depression. Stratified sampling can be performed based on age, gender, and ethnicity to ensure data diversity and representativeness;

[0017] S1.2: Hospital or clinic collaboration: Establish collaboration with mental health institutions, hospitals, or clinics to collect data from patients diagnosed with depression. Ensure approval from the ethics committee;

[0018] S1.3: Public data: Use public social media platforms (Twitter, Instagram) to collect facial images posted by users. Be sure to follow the platform's usage policy;

[0019] S1.4: Image acquisition: High-quality facial images should be captured using professional equipment in a hospital, clinic, or laboratory environment, ensuring diversity under different lighting and expression conditions.

[0020] Furthermore, the S2 is further expressed as:

[0021] S2.1: Network structure: Generate candidate boxes and facial probability maps, and perform more accurate classification and regression on candidate boxes;

[0022] S2.2: Face alignment: Align the detected faces, usually rotating and scaling the faces to a uniform standard position for subsequent processing;

[0023] S2.3: Deep learning features: Use convolutional neural network (CNN) to extract facial features. Commonly used networks include FaceNet and VGGFace.

[0024] Furthermore, the S3 is further expressed as:

[0025] S3.1: Data enhancement: Generate more samples through rotation, flipping, and scaling methods to improve the robustness of the model;

[0026] S3.2: Fully connected layer: Flatten the extracted features and input them into the fully connected layer for final sentiment classification;

[0027] S3.3: Optimizer: The Adam optimizer is commonly used to update weights in an adaptive manner to improve convergence speed;

[0028] S3.4: Multimodal learning: Combining audio and text information to improve emotion recognition accuracy.

[0029] Furthermore, the S4 is further expressed as:

[0030] S4.1: Data sources: Use annotated datasets, such as electronic health records (EHR), mental health questionnaires, social media posts, and surveys;

[0031] S4.2: Data annotation: Ensure that the samples in the dataset have been labeled as depressed or non-depressed. The annotation can be based on expert assessment or self-report questionnaires (PHQ-9, BDI);

[0032] S4.3: Feature Engineering: For text data, you can use TF-IDF, Word2Vec or BERT methods to extract features;

[0033] S4.4: Choose an optimization algorithm: random search, grid search, or Bayesian optimization to find the best hyperparameter combination.

[0034] Furthermore, the S5 is further expressed as:

[0035] S5.1: truePositive (TP): the number of positive examples that are actually positive and correctly predicted by the model;

[0036] S5.2: [\text{Accuracy}=\frac{TP+TN}{TP+TN+FP+FN}];

[0037] S5.3: [\text{Recall}=\frac{TP}{TP+FN}].

[0038] Furthermore, the S6 is further expressed as:

[0039] S6.1: User goal: Allow users to obtain a preliminary assessment of depression risk by uploading facial images;

[0040] S6.2: Health goal: Provide early identification of depression and help users seek professional help in a timely manner;

[0041] S6.3: User registration and login: Users can register and login using email, social media account or mobile phone number;

[0042] S6.4: Result feedback: The evaluation results will be presented to users in a user-friendly interface, and recommended resources, mental health articles, hotlines, and professional consulting services will be provided.

[0043] Furthermore, the S7 is further expressed as:

[0044] S7.1: Transparency: Before conducting facial recognition and depression identification, ensure that participants fully understand the research purpose, process and potential risks;

[0045] S7.2: Data anonymization: The collected data should be anonymized to avoid identifying specific individuals;

[0046] S7.3: Secure storage: Ensure that all data is stored in a secure environment and use encryption technology to prevent data leakage;

[0047] S7.4: Identify potential risks: Before the start of the study, assess the potential psychological and emotional risks of facial recognition and depression detection, such as causing anxiety or mood swings in participants.

[0048] Furthermore, the S8 is further expressed as:

[0049] S8.1: Emotion recognition: Identify the emotions that need to be monitored, such as anxiety, depression, and stress;

[0050] S8.2: Mobile apps: Use mental health apps such as Moodfit and Headspace, which provide mood tracking and feedback.

[0051] S8.3: Frequency: Set the frequency of monitoring, such as daily, weekly, or monthly. Frequent monitoring can help capture changes in mood;

[0052] S8.4: Emotional Data Analysis: Use data analysis tools to analyze collected emotional data and identify emotional trends and fluctuations.

[0053] Furthermore, the S9 is further expressed as:

[0054] S9.1: Data sources: New data can come from multiple channels such as user interaction, sensors, and web crawlers. The collected new data can reflect current trends, behaviors, and changes;

[0055] S9.2: Incremental learning: Incremental learning is performed on the basis of existing models, using new data for training. This method avoids training from scratch, saving time and resources;

[0056] S9.3: New algorithms and architectures: As research progresses, new algorithms (more advanced deep learning architectures, transfer learning) may improve model performance. Regular evaluation and integration of these new technologies is necessary;

[0057] S9.4: Cross-Validation: Use cross-validation techniques to evaluate the new model to ensure that it performs better than the old model on unseen data.

[0058] The present invention has the following beneficial effects:

[0059] 1. The present invention collects samples: a large-scale facial image dataset is collected, including images of healthy individuals and patients with depression, so that the operator can quickly identify the characteristics of the samples (healthy individuals and patients with depression). The research can focus on specific research objectives and improve the relevance of the research.

[0060] 2. The present invention uses face detection: uses face detection algorithms (Haar cascade, MTCNN) to identify the facial area in the image, and extracts facial features so that the operator can quickly generate multiple candidate boxes that may contain faces by using target detection algorithms (YOLO, SSD). These candidate boxes are generated to quickly locate the possible face areas in the image.

[0061] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0063] Figure 1 It is a flowchart of the present invention. DETAILED DESCRIPTION

[0064] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0065] See also Figure 1 The present invention is an intelligent method for facial recognition of depression, comprising the following steps:

[0066] S1: Sample collection: Collect a large-scale facial image dataset, including images of healthy individuals and patients with depression. It can be obtained through clinical data, social media or public datasets;

[0067] S2: Face detection: Use face detection algorithms (Haar cascade, MTCNN) to identify the facial area in the image and extract facial features;

[0068] S3: Emotional feature extraction: Use deep learning models (convolutional neural networks, CNN) to extract facial expression features. These features can reflect emotional states;

[0069] S4: Training model: Use the labeled dataset to train the model and optimize the parameters to improve the recognition accuracy of depression;

[0070] S5: Performance evaluation: Use accuracy, recall, and F1-score indicators to evaluate the performance of the model. The confusion matrix can be used to analyze the classification results;

[0071] S6: Build an application: Develop a user-friendly application that enables users to upload facial images and obtain depression risk assessment results;

[0072] S7: Ethical review: Ensure that ethical standards are followed when conducting facial recognition and depression identification to avoid negative impacts on individuals;

[0073] S8: User tracking: Regularly monitor the user's emotional changes and provide corresponding mental health support;

[0074] S9: Model Updates: As new data is collected and technology develops, the model is updated regularly to improve recognition accuracy and reliability.

[0075] S1 is further expressed as:

[0076] S1.1: Sample population definition: Determine the characteristics of the target sample, including healthy individuals and patients with depression. Stratified sampling can be performed based on age, gender, and ethnicity to ensure data diversity and representativeness;

[0077] S1.2: Hospital or clinic collaboration: Establish collaboration with mental health institutions, hospitals, or clinics to collect data from patients diagnosed with depression. Ensure approval from the ethics committee;

[0078] S1.3: Public data: Use public social media platforms (Twitter, Instagram) to collect facial images posted by users. Be sure to follow the platform's usage policy;

[0079] S1.4: Image acquisition: High-quality facial images should be captured using professional equipment in a hospital, clinic, or laboratory environment, ensuring diversity under different lighting and expression conditions.

[0080] The role of sample group definition: Clarity of goals: By clarifying the characteristics of the sample (healthy individuals and patients with depression), the research can focus on specific research goals and improve the relevance of the research. Diversity and representativeness: Stratified sampling is performed based on age, gender, and ethnicity to ensure the diversity of the sample and enhance the universality and representativeness of the research results. This helps to reveal the differences in facial expressions among different groups. Data quality: The definition of the sample group helps to improve data quality and make the results more credible, thereby better providing a basis for clinical application. The role of hospital or clinic cooperation: Data collection: Establishing a cooperative relationship with mental health institutions, hospitals, or clinics can directly collect real and valid data from patients who have been diagnosed with depression. This cooperation helps to obtain high-quality samples. Ethical compliance: Ensure the approval of the ethics committee, ensure the ethical and legal compliance of the research, and protect the privacy and rights of patients. This is a basic requirement for conducting medical research. Clinical background: The cooperation between hospitals and clinics not only helps with data collection, but also provides professional clinical background information to enhance the scientific nature of the research. The role of data sharing: Rich data sources: Using social media platforms (Twitter, Instagram) to collect facial images posted by users can obtain rich, real-time, and natural facial data. Sample expansion: Social media data can supplement the data collected in hospitals or clinics, increase the sample size, and improve the statistical power of the research. Practical application: Analyzing facial images on social media can help researchers understand the differences between the performance of patients with depression and healthy individuals in different social situations. The role of image acquisition: High-quality data: Use professional equipment to take high-quality facial images in hospitals, clinics, or laboratory environments to ensure the accuracy and analyzability of the data. Diverse conditions: Ensure that images are collected under different lighting and expression conditions, which helps analyze the changes in facial expressions in different situations and improve the robustness of the model. Standardized process: Systematic image acquisition methods can provide a standardized basis for subsequent data analysis, which helps to achieve consistency and repeatability. These four steps play a fundamental role in the research, ensuring the diversity of samples, the legitimacy and quality of data, and at the same time laying a good foundation for in-depth analysis of the research. Through the effective implementation of these steps, researchers can gain a more comprehensive understanding of the manifestations of depression, explore the relationship between facial expressions and psychological states, and provide new perspectives and bases for clinical treatment and prevention.

[0081] S2 is further expressed as:

[0082] S2.1: Network structure: Generate candidate boxes and facial probability maps, and perform more accurate classification and regression on candidate boxes;

[0083] S2.2: Face alignment: Align the detected faces, usually rotating and scaling the faces to a uniform standard position for subsequent processing;

[0084] S2.3: Deep learning features: Use convolutional neural network (CNN) to extract facial features. Commonly used networks include FaceNet and VGGFace.

[0085] Network structure generates candidate boxes and facial probability maps. Generate candidate boxes: In an image, multiple candidate boxes are generated by using object detection algorithms (YOLO, SSD), which may contain faces. These candidate boxes are generated to quickly locate possible face areas in the image. Facial probability map: The facial probability map output by the neural network (FasterR-CNN) can indicate the probability of each candidate box containing a face. This process usually involves using convolutional layers to extract image features, thereby providing a basis for subsequent classification and regression. Function: Accurate classification and regression: The generated candidate boxes will be sent to a more accurate classifier (deep learning model) for processing to confirm whether the box contains a face, and further regress to optimize the position and size of the candidate boxes. This significantly improves the accuracy of face detection. Face alignment aligns the detected faces to a unified standard position: The face alignment process usually includes rotating and scaling the detected faces to a uniform position and size, making subsequent processing (feature extraction, sentiment analysis) more consistent and efficient. This can be achieved through feature point detection (eyes, nose, mouth), ensuring that the face is within the standard frame. Effect: Improve processing accuracy: Through alignment, the key features of the face (eyes, mouth) will be located in similar positions, reducing interference caused by changes in posture and expression, ensuring that subsequent models are more robust and accurate during processing, and reducing computational complexity: Uniform face position and size can reduce the complexity of the model, thereby improving computational efficiency. Deep learning features use convolutional neural networks (CNN) to extract facial features. Feature extraction: Use FaceNet and VGGFace networks for feature extraction. These networks extract high-level features from images through multi-layer convolution and pooling operations. These features can effectively describe different attributes of faces (gender, age, emotion). Function: Strong feature expression ability: Deep learning models can automatically learn and extract important features without manually designing feature extraction algorithms, which makes the model perform well in different face recognition tasks and improves face recognition accuracy: By training on a large amount of data, these CNN models can learn rich facial features, thereby effectively distinguishing different faces in the feature space and improving the accuracy and robustness of face recognition. In summary, the candidate box generation and probability map output of the network structure provide preliminary positioning and confidence assessment for face detection, while face alignment ensures the consistency and comparability of face features, and deep learning feature extraction provides powerful feature representation capabilities. These three steps complement each other and form the basis of modern face recognition systems, greatly improving the overall performance and practicality of the system.

[0086] S3 is further expressed as:

[0087] S3.1: Data enhancement: Generate more samples through rotation, flipping, and scaling methods to improve the robustness of the model;

[0088] S3.2: Fully connected layer: Flatten the extracted features and input them into the fully connected layer for final sentiment classification;

[0089] S3.3: Optimizer: The Adam optimizer is commonly used to update weights in an adaptive manner to improve convergence speed;

[0090] S3.4: Multimodal learning: Combining audio and text information to improve emotion recognition accuracy.

[0091] Data enhancement role: Improve data diversity: Modify the original data through rotation, flipping, and scaling techniques to generate more samples. This is especially important for deep learning models, because models usually need a lot of training data to learn complex patterns. Improve robustness: Data enhancement can make the model more robust when facing different inputs. For example, by rotating an image, the model can better recognize objects regardless of their orientation. Reduce overfitting: Increasing the number of samples helps the model not to remember specific features of the training data during training, thereby improving its generalization ability. The role of the fully connected layer: Feature flattening and integration: The fully connected layer flattens the features extracted by the convolutional layer or other feature extraction layer to form a one-dimensional vector. This step is to map complex high-dimensional features to low-dimensional space to facilitate subsequent classification processing. Final sentiment classification: The fully connected layer performs linear transformation through the learned weights, and finally outputs the probability of each category (sentiment classification: positive, negative, neutral), so that the model can accurately identify sentiment. The role of the optimizer: Adaptive update weights: The Adam optimizer combines the advantages of the momentum method and adaptive learning rate, and can dynamically adjust the learning rate according to the historical gradient information of each parameter, so that it is more efficient to find the optimal solution on the complex loss surface. Improve the convergence speed: Compared with the traditional stochastic gradient descent method, Adam can converge to the global optimal solution faster, which is especially important for training deep neural networks and can effectively reduce training time. The role of multimodal learning: Information fusion: Combining audio and text information can capture emotional features more comprehensively. Audio information can provide the speaker's tone and emotional tone, while text information contains specific content and semantic information. Improve recognition accuracy: By fusing information from multiple modalities, the model can better understand and recognize emotions. For example, in sentiment analysis, the tone of a sentence may contradict its text content. Combining audio information can help the model make more accurate judgments. In summary, the above modules play a key role in sentiment recognition tasks. Data enhancement provides richer training data, the fully connected layer is responsible for the final classification task, the optimizer ensures that the model converges quickly and effectively, and multimodal learning improves the accuracy of sentiment recognition by integrating information from different sources. Through reasonable combination and design, these modules jointly improve the performance and robustness of the model.

[0092] S4 is further expressed as:

[0093] S4.1: Data sources: Use annotated datasets, such as electronic health records (EHR), mental health questionnaires, social media posts, and surveys;

[0094] S4.2: Data annotation: Ensure that the samples in the dataset have been labeled as depressed or non-depressed. The annotation can be based on expert assessment or self-report questionnaires (PHQ-9, BDI);

[0095] S4.3: Feature Engineering: For text data, you can use TF-IDF, Word2Vec or BERT methods to extract features;

[0096] S4.4: Choose an optimization algorithm: random search, grid search, or Bayesian optimization to find the best hyperparameter combination.

[0097] The role of data sources: Diversity and representativeness: Using multiple data sources (electronic health records, mental health questionnaires, social media posts, questionnaires) can ensure data diversity and help the model generalize better to different individuals and environments. This diversity helps capture the different manifestations and characteristics of depression. Data richness: Data from different sources provide different types of information and may be more accurate than clinical data, while social media data may reflect individual mood changes. Combining this information can provide a more comprehensive understanding. The role of data annotation: Accuracy: It is crucial to label samples in the dataset as depressed or non-depressed because machine learning models rely on accurate labels for training. Labeling can be performed by expert assessments (e.g. mental health professionals) or using standardized self-report questionnaires (PHQ-9, BDI). Improve model performance: Well-labeled datasets can improve the training effect and final performance of the model and reduce misclassification. The role of feature engineering: Data conversion: Feature engineering is the process of converting raw data into a format suitable for processing by machine learning models. For text data, TF-IDF, Word2Vec or BERT methods can be used to effectively extract features and capture the semantic information of the text. TF-IDF: By measuring the importance of words in the document, it helps to screen out keywords that are helpful for classification. Word2Vec: Converts vocabulary into vectors, retains the semantic relationship between words, and helps understand the context. BERT: Obtains richer contextual information through pre-trained deep learning models, which is suitable for complex text understanding tasks. Improve the model's understanding ability: Through effective feature extraction, the model can better learn and understand important patterns in the data, thereby improving classification accuracy. The role of selecting optimization algorithms: Model tuning: Optimization algorithms (random search, grid search, or Bayesian optimization) are used to automatically find the best combination of hyperparameters, which have an important impact on model performance. By systematically adjusting the parameters, the accuracy and stability of the model can be effectively improved. Random search: Explore the hyperparameter space by randomly selecting hyperparameter combinations, which is usually more efficient. Search: Train by traversing all possible hyperparameter combinations to ensure that the best combination is found, but the computational cost is high. Bayesian optimization: Select hyperparameters based on probability models, which can achieve better results in less training, especially suitable for high-dimensional parameter spaces. In summary, through the above steps, you can build an effective depression identification model. The diversity and representativeness of data sources ensure the wide applicability of the model, accurate data labeling improves the quality of model training, feature engineering enables the model to effectively understand and process data, and optimization algorithms provide a systematic approach to model tuning. Combining these steps, a machine learning model with superior performance can be built to facilitate early identification and intervention of depression.

[0098] S5 is further expressed as:

[0099] S5.1: truePositive (TP): the number of positive examples that are actually positive and correctly predicted by the model;

[0100] S5.2: [\text{Accuracy}=\frac{TP+TN}{TP+TN+FP+FN}];

[0101] S5.3: [\text{Recall}=\frac{TP}{TP+FN}];

[0102] [\text{F1-score}=2\cdot\frac{\text{Precision}\cdot\text{Recall}}{\text{Precision}+\text{Recall}}].

[0103] True Positive (TP) definition: the number of positive examples that are actually positive and correctly predicted as positive by the model, Function: TP measures the ability of the model in detecting positive classes (e.g., disease, fraud). The higher the TP, the more accurate the model is in identifying positive classes, Accuracy formula: [\text{Accuracy}=\frac{TP+TN}{TP+TN+FP+FN}]TN: True Negative (True Negative), the number of negative examples that are actually predicted as negative, FP: False Positive (False Positive), the number of negative examples that are actually predicted as positive, FN: False Negative (False Negative), the number of positive examples that are actually predicted as negative, Function: Accuracy is the proportion of correct predictions among all predictions, giving an overall assessment. Although it is a commonly used metric, it can be misleading in the case of class imbalance. For example, in an extremely unbalanced dataset (95% negative examples and 5% positive examples), even if the model only predicts all negative examples, it can still get a high accuracy rate. Recall formula: [\text{Recall}=\frac{TP}{TP+FN}] Function: Recall (also called sensitivity or true positive rate) measures the ability of the model to identify positive examples. It shows how many of all actual positive examples are successfully identified by the model. The higher the recall rate, the less likely the model is to miss positive examples. For some applications (disease screening), recall is more important than precision. The F1-score formula is: [\text{F1-score}=2\cdot\frac{\text{Precision}\cdot\text{Recall}}{\text{Precision}+\text{Recall}}] Precision is another key metric, defined as: [\text{Precision}=\frac{TP}{TP+FP}] Role: F1-score is the harmonic mean of precision and recall, providing a single score to balance the two metrics. F1-score is particularly useful when there is a class imbalance because it takes into account false positives and false negatives. The value of F1-score ranges from 0 to 1. The higher the value, the better the model performance. In summary, TP directly measures the recognition ability of the model; accuracy gives an overall evaluation, but may not be reliable enough in imbalanced data; recall emphasizes the recognition ability of the positive class and is suitable for scenarios that require high recall; F1-score provides a comprehensive evaluation of precision and recall, which is suitable for scenarios with imbalanced categories. Through these indicators, we can more comprehensively understand and evaluate the performance of the classification model and select the model that best suits a specific application.

[0104] S6 is further expressed as:

[0105] S6.1: User goal: Allow users to obtain a preliminary assessment of depression risk by uploading facial images;

[0106] S6.2: Health goal: Provide early identification of depression and help users seek professional help in a timely manner;

[0107] S6.3: User registration and login: Users can register and login using email, social media account or mobile phone number;

[0108] S6.4: Result feedback: The evaluation results will be presented to users in a user-friendly interface, and recommended resources, mental health articles, hotlines, and professional consulting services will be provided.

[0109] User Target Role: By uploading a facial image, users can get a preliminary assessment of their risk of depression. The main goals of this feature are: Convenience: Users do not need to fill out cumbersome questionnaires, they only need to upload a facial image to be assessed, which lowers the threshold for participation, Privacy Protection: The user's facial image analysis can be performed without revealing their identity, which increases the user's privacy protection, Quick Feedback: Using computer vision and machine learning technology, the system can quickly analyze images and give assessment results, so that users can quickly understand their mental health status, Health Target Role: Provide early identification of depression, aiming to: Early intervention: By identifying the risk of depression early, help users seek professional help in time, and possibly avoid worsening of the disease, Raise awareness: Raise users' awareness of depression and encourage more people to pay attention to their own and others' mental health, Promote treatment: Provide users with follow-up resources and suggestions to help them get the necessary psychological support and treatment, User Registration and Login Role: Provide a variety of registration and login methods to ensure a smooth user experience, specific functions include: Diversity: Users can register and log in via email, social media account or mobile phone number to meet the preferences of different users. Convenience: Social media login can quickly complete the registration process and reduce the user's entry barrier. Account management: Users can easily manage personal information to ensure a personalized experience in subsequent use. Result feedback: Display the evaluation results in a friendly interface and provide recommended resources, including: User-friendly interface: Through clear and concise design, it is easy for users to understand the evaluation results and reduce users' anxiety. Rich information: Provide relevant mental health articles, hotlines and professional consulting services to help users better understand and deal with their mental health problems. Follow-up support: By recommending professional services, users are encouraged to actively seek help and provide users with practical solutions. The combined effect of these functions is to enhance users' attention to mental health, provide convenient risk assessment, promote early intervention and treatment, and ultimately improve users' mental health. In this way, the platform not only provides users with a convenient tool, but also makes positive contributions to mental health education and support.

[0110] S7 is further expressed as:

[0111] S7.1: Transparency: Before conducting facial recognition and depression identification, ensure that participants fully understand the research purpose, process and potential risks;

[0112] S7.2: Data anonymization: The collected data should be anonymized to avoid identifying specific individuals;

[0113] S7.3: Secure storage: Ensure that all data is stored in a secure environment and use encryption technology to prevent data leakage;

[0114] S7.4: Identify potential risks: Before the start of the study, assess the potential psychological and emotional risks of facial recognition and depression detection, such as causing anxiety or mood swings in participants.

[0115] The role of transparency: Informed consent: Ensure that participants understand the purpose, process and potential risks of the research so that they can make an informed choice as to whether to participate in the research. This is a basic requirement for ethical research. Enhance trust: Through transparent communication, participants' trust in the research team is enhanced, which helps to more realistically reflect the research data. Reduce psychological burden: Clear research purposes and processes can reduce participants' anxiety and make them feel more comfortable during participation. The role of data anonymization: Protect privacy: Through anonymization, specific individuals can be avoided from being identified, thereby protecting the personal privacy and sensitive information of participants. Enhance the ethics of data use: In academic and commercial research, it is very important to protect the identity of participants, which helps to comply with relevant laws and regulations (GDPR). Promote data sharing: Anonymized data is easier to share and use, which can promote scientific research and data reuse, and increase the impact of research. The role of secure storage: Prevent data leakage: The use of secure storage measures and encryption technology can effectively prevent unauthorized access or leakage of data, protect the information security of participants, and ensure research. Research integrity: Ensuring the security of data during storage and transmission helps maintain the integrity and reliability of data and improve the credibility of research results. Comply with regulatory requirements: Many regions have legal provisions requiring the safe storage of personal data. Complying with these regulations is the legal responsibility of researchers. Identify potential risks: Risk assessment: Before the start of the study, identifying possible psychological and emotional risks (anxiety or mood swings) can help the research team develop coping strategies in advance. Optimize research design: Through risk assessment, the research design can be adjusted to reduce the negative impact on participants and ensure that the research is ethically acceptable. Provide support mechanisms: If the research may cause emotional risks, the research team can provide psychological support or counseling services to participants to help them better deal with potential negative emotions. In summary, these standards are not only a protection of the rights and interests of participants, but also a key measure to improve the quality and ethics of research. Implementing these standards can promote the sustainability and social trust of research, while enhancing the reliability and validity of research results.

[0116] S8 is further expressed as:

[0117] S8.1: Emotion recognition: Identify the emotions that need to be monitored, such as anxiety, depression, and stress;

[0118] S8.2: Mobile apps: Use mental health apps such as Moodfit and Headspace, which provide mood tracking and feedback.

[0119] S8.3: Frequency: Set the frequency of monitoring, such as daily, weekly, or monthly. Frequent monitoring can help capture changes in mood;

[0120] S8.4: Emotional Data Analysis: Use data analysis tools to analyze collected emotional data and identify emotional trends and fluctuations.

[0121] The role of emotion recognition: Clear monitoring goals: Identifying specific emotions (anxiety, depression, stress) helps to develop targeted coping strategies. Personalized intervention: By identifying the main emotional problems faced by individuals, intervention plans can be tailored for them to improve the effectiveness of mental health management. Early warning: Timely identification of the emergence of emotional problems can prompt individuals to take measures to avoid further deterioration of emotional problems. The role of mobile applications: Convenience: Mental health applications Moodfit and Headspace can track emotions at any time and place, increasing user participation. Emotion tracking: These applications usually provide functions that facilitate users to record and reflect on emotions, helping users understand the patterns of emotional fluctuations. Feedback mechanism: Through data feedback and visual charts, users can intuitively see the trend of emotional changes and enhance self-awareness. The role of time and frequency: Capture changes: Set an appropriate monitoring frequency (daily , weekly or monthly) can help users better capture subtle changes in emotions, thereby increasing their sensitivity to their own emotions. Habit formation: Regular monitoring can help users gradually develop the habit of emotional management, enabling them to more actively respond to emotional challenges. Data accumulation: Long-term monitoring can accumulate rich emotional data, providing a basis for subsequent analysis and reflection. The role of emotional data analysis: Identifying trends and fluctuations: Using data analysis tools can help users identify long-term trends and short-term fluctuations in emotions, and then understand the causes of emotions. Scientific decision-making: By analyzing emotional data, users can make more scientific and rational decisions when facing stress in life. Personalized adjustment: Based on the results of data analysis, users can adjust their emotional management strategies and coping methods in a targeted manner to achieve better mental health effects. In summary, the various links of emotion recognition and monitoring complement each other and jointly build an effective mental health management system. By clearly monitoring emotions, using mobile applications, setting monitoring frequencies, and conducting data analysis, individuals can not only better understand and manage their emotions, but also seek professional help when needed, thereby improving their overall mental health.

[0122] S9 is further expressed as:

[0123] S9.1: Data sources: New data can come from multiple channels such as user interaction, sensors, and web crawlers. The collected new data can reflect current trends, behaviors, and changes;

[0124] S9.2: Incremental learning: Incremental learning is performed on the basis of existing models, using new data for training. This method avoids training from scratch, saving time and resources;

[0125] S9.3: New algorithms and architectures: As research progresses, new algorithms (more advanced deep learning architectures, transfer learning) may improve model performance. Regular evaluation and integration of these new technologies is necessary;

[0126] S9.4: Cross-Validation: Use cross-validation techniques to evaluate the new model to ensure that it performs better than the old model on unseen data.

[0127] Data source definition: Data source refers to new data obtained from different channels. These channels can include user interaction (user input, feedback), sensor data (real-time data collected by IoT devices), web crawlers (automatic crawling of website information), Role: Reflect trends: New data can timely reflect market changes, user behavior and social trends, help organizations or systems make real-time decisions, improve the accuracy of the model: By introducing the latest data, the model can better capture the current rules and reduce the deviation caused by outdated data, Diversified data sources: Different data sources can provide richer information, thereby improving the generalization ability and robustness of the model, Incremental learning definition: Incremental learning is a machine learning strategy that continues to train with new data based on existing models without rebuilding the model from scratch, Role: Save time and resources: Avoid retraining the entire model, reduce computing overhead and time cost, especially when the amount of data is huge, Strong adaptability: The model can be continuously updated according to new data to maintain sensitivity and adaptability to new situations, Reduce catastrophic forgetting: Incremental learning can learn new knowledge while retaining old knowledge, thereby preventing the model from forgetting what it has learned before, New algorithm and architecture definition: With the advancement of technology, new algorithms and deep learning architectures (convolutional neural networks, recursive neural networks) continue to emerge. These new technologies can usually achieve better performance on specific tasks. Effects: Performance improvement: New algorithms may provide significant improvements in accuracy, efficiency, and computational complexity, allowing models to perform better when performing specific tasks. Wide applicability: New technologies can be applied to various tasks in different fields, such as image processing and natural language processing. Technology iteration: Regular evaluation and integration of new technologies can keep the model cutting-edge and ensure that it remains competitive in a rapidly changing technological environment. Cross-validation definition: Cross-validation is a statistical method used to evaluate the generalization ability of machine learning models. By dividing the data set into multiple subsets, training and validation are performed on different subsets to obtain the performance of the model on unseen data. Role: Model evaluation: Cross-validation can effectively evaluate the performance of the model on new data, avoid bias caused by the division of a single training set, and prevent overfitting: By training and validating on different data sets, it is possible to effectively detect whether the model is overfitting the training data, thereby improving its performance in practical applications, and selecting the best model: The cross-validation results of different models can be used as a basis for comparison to help select the best model or tune model parameters. Summary Through in-depth analysis of data sources, incremental learning, new algorithms and architectures, and cross-validation, it can be seen that these aspects complement each other and jointly promote the continuous optimization and improvement of machine learning models. Their importance in data collection, model training, technology updates, and evaluation processes cannot be ignored, and they provide a solid foundation for intelligent decision-making and accurate prediction.

[0128] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0129] The preferred embodiments of the present invention disclosed above are only used to help illustrate the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific embodiments described. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and effectiveness.

Claims

1. An intelligent method for facial recognition of depression, characterized by: The following steps are involved: S1: Sample collection: Collect a large-scale facial image dataset, including images of healthy individuals and patients with depression. It can be obtained through clinical data, social media or public datasets; S2: Face detection: Use face detection algorithms (Haar cascade, MTCNN) to identify the facial area in the image and extract facial features; S3: Emotional feature extraction: Use deep learning models (convolutional neural networks, CNN) to extract facial expression features. These features can reflect emotional states; S4: Training model: Use the labeled dataset to train the model and optimize the parameters to improve the recognition accuracy of depression; S5: Performance evaluation: Use accuracy, recall, and F1-score indicators to evaluate the performance of the model. The confusion matrix can be used to analyze the classification results; S6: Build an application: Develop a user-friendly application that enables users to upload facial images and obtain depression risk assessment results; S7: Ethical review: Ensure that ethical standards are followed when conducting facial recognition and depression identification to avoid negative impacts on individuals; S8: User tracking: Regularly monitor the user's emotional changes and provide corresponding mental health support; S9: Model Updates: As new data is collected and technology develops, the model is updated regularly to improve recognition accuracy and reliability.

2. According to claim 1, a facial recognition depression intelligent method is characterized in that: The S1 is further expressed as: S1.1: Sample population definition: Determine the characteristics of the target sample, including healthy individuals and patients with depression. Stratified sampling can be performed based on age, gender, and ethnicity to ensure data diversity and representativeness; S1.2: Hospital or clinic collaboration: Establish collaboration with mental health institutions, hospitals, or clinics to collect data from patients diagnosed with depression. Ensure approval from the ethics committee; S1.3: Public data: Use public social media platforms (Twitter, Instagram) to collect facial images posted by users. Be sure to follow the platform's usage policy; S1.4: Image acquisition: High-quality facial images should be captured using professional equipment in a hospital, clinic, or laboratory environment, ensuring diversity under different lighting and expression conditions.

3. According to claim 1, a facial recognition depression intelligent method is characterized in that: The S2 is further expressed as: S2.1: Network structure: Generate candidate boxes and facial probability maps, and perform more accurate classification and regression on candidate boxes; S2.2: Face alignment: Align the detected faces, usually rotating and scaling the faces to a uniform standard position for subsequent processing; S2.3: Deep learning features: Use convolutional neural network (CNN) to extract facial features. Commonly used networks include FaceNet and VGGFace.

4. The method for facial recognition of depression according to claim 1, characterized in that: The S3 is further expressed as: S3.1: Data enhancement: Generate more samples through rotation, flipping, and scaling methods to improve the robustness of the model; S3.2: Fully connected layer: Flatten the extracted features and input them into the fully connected layer for final sentiment classification; S3.3: Optimizer: The Adam optimizer is commonly used to update weights in an adaptive manner to improve convergence speed; S3.4: Multimodal learning: Combining audio and text information to improve emotion recognition accuracy.

5. The method for facial recognition of depression according to claim 1, characterized in that: The S4 is further expressed as: S4.1: Data sources: Use annotated datasets, such as electronic health records (EHR), mental health questionnaires, social media posts, and surveys; S4.2: Data annotation: Ensure that the samples in the dataset have been labeled as depressed or non-depressed. The annotation can be based on expert assessment or self-report questionnaires (PHQ-9, BDI); S4.3: Feature Engineering: For text data, you can use TF-IDF, Word2Vec or BERT methods to extract features; S4.4: Choose an optimization algorithm: random search, grid search, or Bayesian optimization to find the best hyperparameter combination.

6. The method for facial recognition of depression according to claim 1, characterized in that: The S5 is further expressed as: S5.1: truePositive (TP): the number of positive examples that are actually positive and correctly predicted by the model; S5.2: [\text{Accuracy}=\frac{TP+TN}{TP+TN+FP+FN}]; S5.3: [\text{Recall}=\frac{TP}{TP+FN}].

7. The method for facial recognition of depression according to claim 1, characterized in that: The S6 is further expressed as: S6.1: User goal: Allow users to obtain a preliminary assessment of depression risk by uploading facial images; S6.2: Health goal: Provide early identification of depression and help users seek professional help in a timely manner; S6.3: User registration and login: Users can register and login using email, social media account or mobile phone number; S6.4: Result feedback: The evaluation results will be presented to users in a user-friendly interface, and recommended resources, mental health articles, hotlines, and professional consulting services will be provided.

8. The method for facial recognition of depression according to claim 1, characterized in that: The S7 is further expressed as: S7.1: Transparency: Before conducting facial recognition and depression identification, ensure that participants fully understand the research purpose, process and potential risks; S7.2: Data anonymization: The collected data should be anonymized to avoid identifying specific individuals; S7.3: Secure storage: Ensure that all data is stored in a secure environment and use encryption technology to prevent data leakage; S7.4: Identify potential risks: Before the start of the study, assess the potential psychological and emotional risks of facial recognition and depression detection, such as causing anxiety or mood swings in participants.

9. The intelligent method for facial recognition of depression according to claim 1, characterized in that: The S8 is further expressed as: S8.1: Emotion recognition: Identify the emotions that need to be monitored, such as anxiety, depression, and stress; S8.2: Mobile apps: Use mental health apps such as Moodfit and Headspace, which provide mood tracking and feedback. S8.3: Frequency: Set the frequency of monitoring, such as daily, weekly, or monthly. Frequent monitoring can help capture changes in mood; S8.4: Emotional Data Analysis: Use data analysis tools to analyze collected emotional data and identify emotional trends and fluctuations.

10. The intelligent method for facial recognition of depression according to claim 1, characterized in that: The S9 is further expressed as: S9.1: Data sources: New data can come from multiple channels such as user interaction, sensors, and web crawlers. The collected new data can reflect current trends, behaviors, and changes; S9.2: Incremental learning: Perform incremental learning based on the existing model and use new data for training. This approach avoids training from scratch, saving time and resources; S9.3: New algorithms and architectures: As research progresses, new algorithms (more advanced deep learning architectures, transfer learning) may improve model performance. Regular evaluation and integration of these new technologies is necessary; S9.4: Cross-Validation: Use cross-validation techniques to evaluate the new model to ensure that it performs better than the old model on unseen data.

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