Classroom teaching-oriented student dynamic expression and action recognition and evaluation system and method

Abstract

The invention relates to the technical field of computer vision and artificial intelligence, and discloses a classroom teaching-oriented student dynamic expression and action recognition evaluation system and method, and the system comprises a video collection module, an image processing module, an expression and action recognition module, an evaluation analysis module, and a feedback output module. The video acquisition module is used for acquiring video data of a classroom site, the image processing module is used for preprocessing the video data, the expression and action recognition module is used for recognizing expressions and actions of students by using a deep learning algorithm, and the evaluation analysis module is used for evaluating learning states of the students. And the feedback output module outputs the evaluation result to a teacher in a chart or report form. By integrating video acquisition, image processing, deep learning recognition, data analysis and other technologies, expressions and actions of students are monitored and analyzed in real time, so that more accurate and timely learning state feedback is provided for teachers, the teachers can adjust the teaching method in time according to the results, and the classroom teaching effect can be effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of computer vision and artificial intelligence, and in particular to a system and method for evaluating students' dynamic expression and action recognition in classroom teaching. Background Art

[0002] In traditional classroom teaching, teachers often rely on students' classroom feedback to evaluate their learning status, but this evaluation method usually relies on students' subjective performance, is easily affected by students' emotions, environmental factors, etc., and cannot reflect students' learning status in real time. Most existing learning status evaluation methods rely on regular tests or classroom interactions, lacking continuous monitoring and evaluation of students' learning dynamics. With the development of artificial intelligence technology, how to use computer vision technology to accurately identify students' expressions and movements and then evaluate their learning status has become an important issue in improving the quality of classroom teaching.

[0003] To this end, we propose a student dynamic expression and action recognition and evaluation system and method for classroom teaching. Summary of the invention

[0004] The present invention mainly solves the technical problems existing in the above-mentioned prior art and provides a student dynamic expression and action recognition and evaluation system and method for classroom teaching.

[0005] In order to achieve the above-mentioned purpose, the present invention adopts the following technical scheme, which is a student dynamic expression and action recognition and evaluation system for classroom teaching, including a video acquisition module, an image processing module, an expression and action recognition module, an evaluation and analysis module and a feedback output module. The video acquisition module is used to collect video data of the classroom scene, the image processing module pre-processes the video data, the expression and action recognition module uses a deep learning algorithm to recognize students' expressions and actions, the evaluation and analysis module evaluates students' learning status, and the feedback output module outputs the evaluation results to teachers in the form of charts or reports. The evaluation and analysis module comprehensively evaluates students' learning status indicators such as concentration, learning involvement, comprehension, emotional response, etc. based on changes in students' expressions and actions, and provides guidance and suggestions to teachers through real-time feedback.

[0006] As a preferred technical solution of the present invention, the video acquisition module is a camera fixedly installed in the classroom or a mobile device held by teachers and students, which is used to record the dynamic behavior of students in real time.

[0007] As a preferred technical solution of the present invention, the image processing module performs preprocessing operations of noise reduction, brightness and contrast adjustment, edge enhancement, and face or action area detection on the video data to improve the precision and accuracy of image processing.

[0008] As a preferred technical solution of the present invention, the expression and action recognition module uses a convolutional neural network (CNN) or a recurrent neural network (RNN) to analyze the processed images to identify students' facial expressions and body movements, and further distinguish learning emotions (such as happiness, confusion, anxiety, etc.), classroom participation (such as nodding, gestures, etc.) and attention changes.

[0009] As a preferred technical solution of the present invention, the evaluation and analysis module evaluates the student's learning status, including attention concentration, learning engagement, comprehension assessment and emotional response, based on the recognition results and preset evaluation criteria.

[0010] As a preferred technical solution of the present invention, the feedback output module displays the evaluation results to the teacher in the form of real-time charts or after-class reports, and can provide instant feedback on changes in students' learning status (such as concentration, comprehension, etc.) in real-time classrooms to assist teachers in adjusting teaching strategies.

[0011] The method for evaluating students' dynamic expression and action recognition in classroom teaching includes the above-mentioned system for evaluating students' dynamic expression and action recognition in classroom teaching, and specifically includes the following steps:

[0012] S1: Start the video acquisition module to record classroom video data in real time;

[0013] S2: The video data is transmitted to the image processing module for preprocessing, including noise reduction, brightness and contrast adjustment, edge enhancement, face area detection and other operations;

[0014] S3: Send the preprocessed image data to the expression and action recognition module for analysis to identify students’ facial expressions, body movements, and emotional reactions;

[0015] S4: Comprehensively evaluate students’ learning status such as concentration, learning engagement, comprehension, and emotional changes through the evaluation and analysis module;

[0016] S5: Feedback the evaluation results to the teacher through the feedback output module, and provide real-time suggestions or generate after-class reports.

[0017] As a further limitation of the above scheme, the preprocessing in S2 includes noise reduction, brightness and contrast adjustment, and edge enhancement pre-operation to improve the accuracy of image processing and ensure high-quality data input in the subsequent analysis stage.

[0018] As a further limitation of the above scheme, S4 evaluates students' concentration (i.e., students' concentration, manifested by focused eyes, frequent nodding, active interaction, etc.), learning engagement (such as students' smile, leaning forward, quick response, etc., indicating the degree of students' participation in class), comprehension (such as whether students show a confused frown or an understanding smile, shining eyes, etc.) and emotional state (such as emotional reactions such as anxiety and boredom) based on changes in students' expressions and movements.

[0019] As a further limitation of the above scheme, the evaluation and analysis module also includes a sentiment analysis model, which is used to further evaluate students' emotional fluctuations based on changes in their facial expressions and body movements, and comprehensively analyze their impact on classroom learning status.

[0020] The invention provides a student dynamic expression and action recognition and evaluation system and method for classroom teaching.

[0021] It has the following beneficial effects:

[0022] 1. The student dynamic expression and action recognition and evaluation system and method for classroom teaching can accurately capture and analyze students' facial expressions and body movements through video acquisition and deep learning algorithms, evaluate students' emotions, concentration and learning engagement in real time, and provide personalized learning feedback and support. The system not only improves classroom interactivity and teaching effectiveness, but also helps teachers make flexible teaching adjustments through real-time feedback, thereby improving teaching efficiency, reducing the burden on teachers, and achieving more refined and personalized teaching in the classroom.

[0023] 2. The student dynamic expression and action recognition and evaluation system and method for classroom teaching can capture and analyze students' facial expressions and body movements in real time, and monitor students' learning status without blind spots through the video acquisition module. The system can timely identify students' emotional changes and learning participation, and the feedback results can be quickly transmitted to teachers. This real-time feedback mechanism helps teachers quickly judge students' learning situation in class, especially to promptly discover whether students have comprehension difficulties, emotional fluctuations or lack of concentration, so as to make targeted teaching adjustments.

[0024] 3. This student dynamic expression and action recognition and evaluation system and method for classroom teaching is based on deep learning technology (such as convolutional neural network CNN and recurrent neural network RNN). This system can accurately identify students' facial expressions (such as smiling, frowning, focusing, confusion, etc.) and body movements (such as nodding, raising hands, lowering heads, etc.). These expressions and movements can accurately reflect students' emotional state, concentration and learning engagement. The system provides objective learning status evaluation based on this information.

[0025] 4. The student dynamic expression and action recognition and evaluation system and method for classroom teaching can generate personalized learning reports for each student through real-time monitoring and analysis of students' facial expressions and body language. These reports can record in detail the students' emotional fluctuations, participation, and changing trends in their learning status in class, helping teachers identify students' learning difficulties, interests, and needs. For example, students may show strong interest in certain topics, and the system can provide these details to help teachers adjust their teaching methods to better meet students' personalized needs and improve the pertinence and effectiveness of teaching.

[0026] 5. The student dynamic expression and action recognition and evaluation system and method for classroom teaching can automatically evaluate the learning status of students, reducing the manual observation and analysis of students' performance by teachers in the classroom. Teachers do not need to spend a lot of time judging students' emotions or participation. Instead, they can adjust the teaching content and teaching rhythm in time through the real-time feedback provided by the system to improve the interactivity and effectiveness of the classroom. Through automated evaluation and feedback, the system greatly reduces the management burden of teachers, allowing teachers to focus more on teaching itself, thereby improving overall teaching efficiency.

[0027] 6. The student dynamic expression and action recognition and evaluation system and method for classroom teaching can discover students' potential interests and problems during the learning process and provide timely feedback by continuously monitoring students' emotional changes and participation. This continuous interaction can motivate students to participate in the class more actively and improve their learning motivation. In addition, through teachers' real-time attention to students' learning status and personalized feedback, students can feel more targeted and personalized teaching support, thereby enhancing their learning engagement.

[0028] 7. This student dynamic expression and action recognition evaluation system and method for classroom teaching provides a set of detailed standards for evaluating students' learning status, including but not limited to dimensions such as emotional fluctuations, concentration, and learning engagement.

[0029] 8. The student dynamic expression and action recognition and evaluation system and method for classroom teaching can reduce the classroom management pressure of teachers and improve the quality of classroom: through the automated learning status monitoring and feedback mechanism, the present invention helps teachers reduce a lot of classroom management pressure, and teachers can focus more on the teaching content without paying too much attention to students' individual emotions and behaviors. The system can continuously monitor the students' learning status and provide intuitive feedback. With the auxiliary judgment of the system, teachers can provide personalized guidance to students more accurately. This not only ensures the teaching quality of the classroom, but also improves the efficiency of classroom management. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The structures, proportions, sizes, etc. illustrated in this specification are only used to match the contents disclosed in the specification for people familiar with this technology to understand and read, and are not used to limit the conditions under which the present invention can be implemented, so they have no substantial technical significance.

[0031] Figure 1 The present invention is a system block diagram of the student dynamic expression and action recognition and evaluation system and method for classroom teaching. DETAILED DESCRIPTION

[0032] Example 1

[0033] See also Figure 1 As shown, the student dynamic expression and action recognition evaluation system for classroom teaching includes a video acquisition module, an image processing module, an expression and action recognition module, an evaluation and analysis module and a feedback output module. The video acquisition module is used to collect video data of the classroom scene, the image processing module pre-processes the video data, the expression and action recognition module uses a deep learning algorithm to recognize students' expressions and actions, the evaluation and analysis module evaluates students' learning status, and the feedback output module outputs the evaluation results to teachers in the form of charts or reports. The evaluation and analysis module comprehensively evaluates students' learning status indicators such as concentration, learning involvement, comprehension, emotional response, etc. based on changes in students' expressions and actions, and provides guidance and suggestions to teachers through real-time feedback.

[0034] The video acquisition module is a camera fixedly installed in the classroom or a mobile device held by teachers and students, which is used to record students' dynamic behaviors in real time.

[0035] The image processing module performs preprocessing operations on video data such as noise reduction, brightness and contrast adjustment, edge enhancement, and face or action area detection to improve the precision and accuracy of image processing.

[0036] The expression and action recognition module uses a convolutional neural network (CNN) or a recurrent neural network (RNN) to analyze the processed images to identify students' facial expressions and body movements, and further distinguish learning emotions (such as happiness, confusion, anxiety, etc.), class participation (such as nodding, gestures, etc.) and attention changes.

[0037] The assessment and analysis module evaluates students' learning status based on the recognition results and preset assessment criteria, including concentration, learning engagement, comprehension assessment, and emotional response.

[0038] The feedback output module displays the evaluation results to teachers in the form of real-time charts or after-class reports, and can provide instant feedback on changes in students' learning status (such as concentration, comprehension, etc.) in real-time classes to assist teachers in adjusting teaching strategies.

[0039] The method for evaluating students' dynamic expression and action recognition in classroom teaching includes the above-mentioned system for evaluating students' dynamic expression and action recognition in classroom teaching, and specifically includes the following steps:

[0040] S1: Start the video acquisition module to record classroom video data in real time;

[0041] S2: The video data is transmitted to the image processing module for preprocessing, including noise reduction, brightness and contrast adjustment, edge enhancement, face area detection and other operations;

[0042] S3: Send the preprocessed image data to the expression and action recognition module for analysis to identify students’ facial expressions, body movements, and emotional reactions;

[0043] S4: Comprehensively evaluate students’ learning status such as concentration, learning engagement, comprehension, and emotional changes through the evaluation and analysis module;

[0044] S5: Feedback the evaluation results to the teacher through the feedback output module, and provide real-time suggestions or generate after-class reports.

[0045] Preprocessing in S2 includes noise reduction, brightness and contrast adjustment, and edge enhancement pre-operations to improve the accuracy of image processing and ensure high-quality data input in the subsequent analysis stage.

[0046] In S4, students’ concentration (i.e., how well they concentrate, as evidenced by focused eyes, frequent nodding, active interaction, etc.), learning engagement (such as students’ smiles, leaning forward, quick responses, etc., indicating the extent to which they participate in class), comprehension (such as whether students show a confused frown or an understanding smile, shining eyes, etc.), and emotional state (e.g., emotional reactions such as anxiety and boredom) are assessed based on changes in their facial expressions and movements.

[0047] The evaluation and analysis module also includes a sentiment analysis model, which is used to further evaluate students' emotional fluctuations based on changes in their facial expressions and body movements, and comprehensively analyze their impact on the learning status in class.

[0048] Example 2

[0049] Based on Example 1, in this example, the video acquisition module uses a camera fixed in the classroom to capture students' dynamic performance in the classroom. In this way, the system can monitor the entire classroom from multiple angles in real time, capture students' expressions and movements, and help teachers evaluate students' learning status. The specific implementation steps are as follows:

[0050] S1: Video acquisition. The system installs high-definition cameras in the four corners of the classroom. These cameras can capture students' facial expressions and body movements without blind spots. The cameras transmit video data to the central processing system in real time via the network. The installation position and angle of the cameras are carefully designed to ensure that the video covers the students' faces and bodies, avoiding missing any students' dynamic performance due to position problems.

[0051] S2: Image processing. After receiving the video data, the image processing module will perform a series of preprocessing operations. First, the system will execute a noise removal algorithm to eliminate background noise in the classroom (such as interference caused by other people walking or changes in light outside the window). Then, the system will adjust the brightness and enhance the contrast to optimize the image quality when the shooting environment is too dark or too bright. Finally, edge detection technology is used to enhance key features in the image, such as students' facial features, gestures, body movements, etc., so that the subsequent expression and action recognition module can analyze more accurately.

[0052] S3: Expression and action recognition. The processed image data will be sent to the expression and action recognition module. This module is based on deep learning algorithms (such as convolutional neural networks (CNNs)). The training model can recognize different facial expressions, such as smiling, frowning, confusion, concentration, etc., and can recognize students' body movements, such as raising hands, nodding, shaking heads, sitting postures, etc. Through model training, the system can judge students' emotions and learning participation through facial features and posture features in the image. For example:

[0053] Smiling / light eyes: indicates that students are interested in class content, are in a positive mood, and are highly engaged in learning.

[0054] Frowning / Raised eyebrows: May indicate difficulty or confusion, lower comprehension, and lower concentration in learning.

[0055] Nodding / raising hands: indicates that students actively participate in class interaction, are highly engaged in learning, and are paying attention.

[0056] S4: Learning status assessment, based on the recognized facial expressions and movements, the assessment and analysis module will conduct a comprehensive assessment of the student’s learning status. For example:

[0057] If a student frequently frowns, the system will judge that the student may not understand the class content and appear confused.

[0058] If a student nods or raises his hand frequently, it may mean that the student is actively participating in the class. Through a comprehensive analysis of facial expressions (such as smiling, confused, focused, etc.) and movements (such as nodding, raising hands, lowering heads, etc.), the student's concentration and learning engagement can be assessed.

[0059] 1. Attention concentration assessment criteria:

[0060] (1) High: The student’s eyes are focused, he nods frequently or his eyes are fixed, and his facial expression is relaxed or smiling.

[0061] (2) Moderate: The student is occasionally distracted, occasionally looks down, and has a slightly confused facial expression.

[0062] (3) Low: Students frequently lose focus, have wandering eyes, and frown or blank facial expressions

[0063] 2. Learning engagement evaluation criteria:

[0064] (1) High: Students actively participate in classroom interaction, raise their hands frequently, move their bodies actively, and have happy expressions, smiles, and bright eyes.

[0065] (2) Moderate: The student occasionally participates in the interaction, shows mild interest, and has a calm but inactive facial expression.

[0066] (3) Low: The student barely participates in the interaction, appears anxious or impatient, and has closed body language (e.g., arms crossed).

[0067] S5: Feedback and display. The evaluation results will be presented to teachers in the form of intuitive charts through the feedback output module. The charts can show information such as students' participation, emotional fluctuations, and learning status. Teachers can see the changing trends of students' learning concentration and learning engagement, and can adjust the rhythm and methods of classroom teaching based on this information.

[0068] Through fixed cameras, the system can continuously monitor and analyze students' expressions and movements throughout the class, and promptly identify students' emotional changes and learning status. Teachers can adjust teaching methods based on real-time feedback to improve classroom interactivity and teaching effectiveness.

[0069] Example 3

[0070] Based on Example 1, in this example, the video acquisition module uses smart glasses worn by teachers to record videos. This method can capture students' performance more realistically and flexibly, and can dynamically adjust the camera angle to ensure accurate recognition of students' expressions and movements in a dynamic teaching environment. The specific implementation steps are as follows:

[0071] S1: Video acquisition. Teachers wear specially designed smart glasses with a small HD camera built in them to record students' expressions and movements in real time. Since the camera is installed in front of the teacher's eyes, the glasses can move with the teacher's line of sight and flexibly capture the dynamics of students in the classroom. Smart glasses transmit the collected video stream to the processing center in real time via Bluetooth or Wi-Fi, avoiding the viewing angle limitation that may be caused by the fixed position of traditional cameras.

[0072] S2: Image processing. After receiving the video data, the image processing module performs preprocessing operations. Since the camera worn by the teacher may encounter environmental problems with large changes in light (such as sunlight coming in from the window), the image processing module will adjust the video brightness, contrast and color balance through intelligent algorithms to ensure the clarity and quality of the image. At the same time, the system will use motion compensation algorithms to deal with image jitter problems during the teacher's movement, ensuring that each frame can accurately capture the student's expression and movement.

[0073] S3: Expression and action recognition, the processed image data is sent to the expression and action recognition module. This module combines convolutional neural network (CNN) and recurrent neural network (RNN) to analyze students' facial expressions and body language. By identifying students' emotional reactions (such as concentration, confusion, happiness, irritability, etc.) and body movements (such as raising hands, crossing hands, lowering heads, etc.), the system can judge students' learning status and class participation.

[0074] S4: Learning status assessment, the facial expression and action information output by the recognition module will be transmitted to the evaluation and analysis module for comprehensive analysis. This module not only considers the changes in students' expressions, but also combines action information, such as whether students frequently raise their hands to participate in interactions, whether they bow their heads too much or are distracted, etc. The evaluation and analysis module will determine whether students understand the class content, whether they stay focused, and whether they actively participate in class discussions based on this information, and generate evaluation results in real time.

[0075] S5: Feedback and display, the evaluation and analysis results are displayed to teachers in real time through the feedback output module. In addition to displaying students' learning status through charts, the system can also generate after-class reports, which record in detail each student's emotional fluctuations, participation, and changing trends in learning status in class. Teachers can adjust their teaching strategies based on the content of the report to help students overcome learning difficulties or further stimulate their interest in learning.

[0076] With the convenient device of smart glasses, teachers can more flexibly capture students' dynamic performance in class and provide more personalized feedback. At the same time, the system can accurately evaluate students' learning status in real time, enhancing the interactivity and teaching effect of the class.

[0077] Working principle of the present invention: The system of the present invention collects image data of the classroom scene in real time through the video acquisition module, and pre-processes the image through the image processing module to improve the quality. The processed image is analyzed in the expression and action recognition module through the deep learning algorithm to identify the student's facial expression, body movement and its changes. The evaluation and analysis module evaluates the student's learning status based on the recognition results, focusing on the student's concentration and learning involvement, and presents the evaluation results to the teacher through the feedback output module. Teachers can adjust the teaching methods in time according to these evaluation results to improve the classroom teaching effect.

[0078] The system of the present invention has the following effects:

[0079] 1. Real-time feedback and dynamic monitoring: The system can capture and analyze students' facial expressions and body movements in real time, and monitor students' learning status without blind spots through the video acquisition module. The system can promptly identify students' emotional changes and learning participation, and the feedback results can be quickly transmitted to teachers. This real-time feedback mechanism helps teachers quickly judge students' learning situation in class, especially to promptly discover whether students have comprehension difficulties, emotional fluctuations or lack of concentration, so as to make targeted teaching adjustments.

[0080] 2. Accurate assessment of emotions and learning status: Based on deep learning technology (such as convolutional neural networks (CNN) and recurrent neural networks (RNN), this system can accurately identify students' facial expressions (such as smiling, frowning, focusing, confusion, etc.) and body movements (such as nodding, raising hands, lowering heads, etc.). These expressions and movements can accurately reflect students' emotional state, concentration, and learning engagement. The system provides objective learning status assessment based on this information. For example:

[0081] Smiling / light eyes: This means the student is happy, focused, interested in the class content, and highly engaged in learning;

[0082] Frowning / raising eyebrows: indicates that the student may be confused or do not understand what is being taught, is less engaged in learning, and may need help;

[0083] Frequent nodding / raising of hands: This indicates that students actively participate in class interaction, concentrate and are in a good learning state;

[0084] Looking down / wandering: This indicates that the student is not paying enough attention to the class content and may have learning disabilities or lack of attention.

[0085] This precise emotion recognition and status assessment not only helps teachers understand students' immediate reactions, but also provides quantitative data support based on students' specific states.

[0086] 3. Personalized learning feedback and support: Through real-time monitoring and analysis of students' facial expressions and body language, the present invention can generate personalized learning reports for each student. These reports can record in detail the students' mood swings, participation, and changing trends in their learning status in class, helping teachers identify students' learning difficulties, interests, and needs. For example, students may show strong interest in certain topics, and the system can provide these details to help teachers adjust their teaching methods to better meet students' personalized needs and improve the pertinence and effectiveness of teaching.

[0087] 4. Improve teaching efficiency and classroom interactivity: This system can automatically evaluate students' learning status, reducing the manual observation and analysis of students' performance by teachers in class. Teachers do not need to spend a lot of time judging students' emotions or participation. Instead, they can adjust teaching content and teaching rhythm in time through the real-time feedback provided by the system to improve the interactivity and effectiveness of the class. Through automated evaluation and feedback, the system greatly reduces the management burden of teachers, allowing teachers to focus more on teaching itself, thereby improving overall teaching efficiency.

[0088] 5. Support dynamic adjustment of teaching content and methods: Based on the student emotions and participation data provided by the system, teachers can adjust teaching methods and content more flexibly. For example, if students' emotions are generally confused or anxious, teachers can adjust the class rhythm or provide more guidance and explanations in a timely manner; if students show high participation and concentration, teachers can increase the interactivity and difficulty of the class to maintain students' enthusiasm for learning. The system's feedback supports real-time adjustment of teaching strategies, which helps maximize students' learning effects.

[0089] 6. Enhance students' learning motivation and participation: The present invention can discover students' potential interests and problems during the learning process and provide timely feedback by continuously monitoring students' emotional changes and participation. This continuous interaction can motivate students to participate in class more actively and improve their learning motivation. In addition, through teachers' real-time attention to students' learning status and personalized feedback, students can feel more targeted and personalized teaching support, thereby enhancing students' learning involvement.

[0090] 7. Multi-dimensional student learning status assessment criteria: The present invention provides a set of detailed criteria for assessing students' learning status, including but not limited to dimensions such as mood swings, concentration, and learning engagement. These dimensions are quantified by the following criteria:

[0091] Emotional fluctuations: Changes in students' facial expressions, such as smiling, frowning, and shining eyes, will be recognized and recorded by the system to assess whether the students are interested in the class content or confused.

[0092] Attention concentration: Evaluate students’ attention through their eye direction, whether they frequently look down, and whether they are distracted, to determine whether they are able to maintain their attention.

[0093] Learning engagement: Judge students’ class participation through their body movements (such as raising hands, sitting posture, crossing hands, etc.) and facial expressions (such as concentration, active interaction).

[0094] After comprehensive evaluation of these dimensions, they can fully reflect the students' learning status and help teachers understand the students' learning situation in real time.

[0095] 8. Reduce the classroom management pressure of teachers and improve the quality of classroom: Through the automated learning status monitoring and feedback mechanism, the present invention helps teachers reduce a lot of classroom management pressure. Teachers can focus more on the teaching content without paying too much attention to students' individual emotions and behaviors. The system can continuously monitor the students' learning status and provide intuitive feedback. With the help of the system's auxiliary judgment, teachers can provide more accurate personalized guidance to students. This not only ensures the teaching quality of the classroom, but also improves the efficiency of classroom management.

[0096] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited by the above embodiments, and the above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, and these changes and improvements all fall within the scope of the present invention to be protected.

Claims

1. A student dynamic expression and action recognition and evaluation system for classroom teaching, characterized by: It includes a video acquisition module, an image processing module, an expression and action recognition module, an evaluation and analysis module and a feedback output module. The video acquisition module is used to collect video data of the classroom scene. The image processing module pre-processes the video data. The expression and action recognition module uses a deep learning algorithm to recognize students' expressions and actions. The evaluation and analysis module evaluates students' learning status. The feedback output module outputs the evaluation results to the teacher in the form of charts or reports. The evaluation and analysis module comprehensively evaluates students' learning status indicators such as concentration, learning involvement, comprehension, emotional response, etc. based on changes in students' expressions and actions, and provides guidance and suggestions to teachers through real-time feedback.

2. The student dynamic expression and action recognition and evaluation system for classroom teaching according to claim 1 is characterized by: The video acquisition module is a camera fixedly installed in the classroom or a mobile device held by teachers and students, which is used to record the dynamic behavior of students in real time.

3. The student dynamic expression and action recognition and evaluation system for classroom teaching according to claim 1 is characterized by: The image processing module performs preprocessing operations of noise reduction, brightness and contrast adjustment, edge enhancement, and face or action area detection on the video data to improve the precision and accuracy of image processing.

4. The student dynamic expression and action recognition and evaluation system for classroom teaching according to claim 1 is characterized by: The expression and action recognition module uses a convolutional neural network (CNN) or a recurrent neural network (RNN) to analyze the processed images to identify students' facial expressions and body movements, and further distinguish learning emotions, class participation, and attention changes.

5. The student dynamic expression and action recognition and evaluation system for classroom teaching according to claim 1 is characterized by: The evaluation and analysis module evaluates the student's learning status, including attention concentration, learning engagement, comprehension assessment, and emotional response, based on the recognition results and preset evaluation criteria.

6. The student dynamic expression and action recognition and evaluation system for classroom teaching according to claim 1 is characterized by: The feedback output module displays the evaluation results to the teacher in the form of real-time charts or after-class reports, and can provide instant feedback on changes in students' learning status in real-time classes to assist teachers in adjusting their teaching strategies.

7. A method for evaluating students' dynamic facial expressions and actions in classroom teaching, characterized in that: The student dynamic expression and action recognition and evaluation system for classroom teaching according to any one of claims 1 to 6 specifically comprises the following steps: S1: Start the video acquisition module to record classroom video data in real time; S2: The video data is transmitted to the image processing module for preprocessing, including noise reduction, brightness and contrast adjustment, edge enhancement, face area detection and other operations; S3: Send the preprocessed image data to the expression and action recognition module for analysis to identify students’ facial expressions, body movements, and emotional reactions; S4: Comprehensively evaluate students’ learning status such as concentration, learning engagement, comprehension, and emotional changes through the evaluation and analysis module; S5: Feedback the evaluation results to the teacher through the feedback output module, and provide real-time suggestions or generate after-class reports.

8. The method for evaluating students' dynamic expression and action recognition in classroom teaching according to claim 7 is characterized in that: The preprocessing in S2 includes noise reduction, brightness and contrast adjustment, and edge enhancement pre-operation to improve the accuracy of image processing and ensure high-quality data input in the subsequent analysis stage.

9. The method for evaluating students' dynamic expression and action recognition in classroom teaching according to claim 7, characterized in that: In S4, the student's concentration, learning engagement, comprehension and emotional state are evaluated based on changes in the student's facial expressions and movements.

10. The method for evaluating students' dynamic expression and action recognition in classroom teaching according to claim 7, characterized in that: The evaluation and analysis module also includes a sentiment analysis model, which is used to further evaluate students' emotional fluctuations based on changes in their facial expressions and body movements, and comprehensively analyze their impact on classroom learning status.

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