Abstract
In this paper, a three-dimensional anisotropic diffusion equation is used to conduct an in-depth study and analysis of students’ concentration in video recognition in English teaching classrooms. A multifeature fusion face live detection method based on diffusion model extracts Diffusion Kernel (DK) features and depth features from diffusion-processed face images, respectively. DK features provide a nonlinear description of the correlation between successive face images and express face image sequences in the temporal dimension; depth features are extracted by a pretrained depth neural network model that can express the complex nonlinear mapping relationships of images and reflect the more abstract implicit information inside face images. To improve the effectiveness of the face image features, the extracted DK features and depth features are fused using a multicore learning method to obtain the best combination and the corresponding weights. The two features complement each other, and the fused features are more discriminative, which provides a strong basis for the live determination of face images. Experiments show that the method has excellent performance and can effectively discriminate the live nature of faces in images and resist forged face attacks. Based on the above face detection and expression recognition algorithms, the classroom concentration analysis system based on expression recognition is designed to achieve real-time acquisition and processing of classroom images, complete student classroom attendance records using face detection and face recognition methods, and analyze students’ concentration from the face integrity and facial expression of students facing the blackboard by combining face detection and expression recognition to visualize and display students’ classroom data for teachers, students, and parents with more data support and help.
Highlights
Current face recognition systems are not strong enough in effectively distinguishing between live and nonlive face data, i.e., they encounter challenges in determining whether the face image acquired by the system is from a real legitimate user or a forged face, and face live detection comes into being [1]
The training and optimization of the expression recognition algorithm and the comparative analysis of the experimental results show that the two models proposed in this paper outperform the baseline model on both the public and private test sets, and the improved method based on the VGG model performs better on the dataset compared to the model with deep separable convolution
From the overall class concentration line graph, it can be seen that in the first and second class around 200 tests and after 160 tests, the overall concentration situation of the class was poor in this period because it was tested every second, which means that the overall concentration of the class was low in the first seven minutes of class and the first five minutes of class, but compared to the first class, the overall concentration situation of the class in the second class was better
Summary
Current face recognition systems are not strong enough in effectively distinguishing between live and nonlive face data, i.e., they encounter challenges in determining whether the face image acquired by the system is from a real legitimate user or a forged face, and face live detection comes into being [1]. Through the changes of students’ expressions and head postures in the classroom learning process, we can judge the changes in student’s concentration in the learning process and set up real-time prompting means or postclass feedback so that students can understand the problems in classroom learning and improve them in time; at the same time, we can use the concentration evaluation system based on facial expressions and head postures to provide reference basis for teachers’ teaching evaluation and teaching methods [3]. It makes it possible to rationalize the teaching improvement plan according to students’ characteristics. To integrate the above issues, it is necessary to develop a system to assist teachers in classroom attendance and classroom concentration analysis with the characteristics of classroom teaching management and provide timely feedback to teachers, students, and parents so that the three parties can jointly supervise and improve the quality of teaching, forming a positive and virtuous cycle
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