Class attending quality detection method based on face recognition

Abstract

The invention provides a class attending quality detection method based on face recognition. The method comprises the following steps: preliminarily obtaining students who raise the head in class through face recognition under a convolutional neural network; performing region division analysis on all students, dividing an image into three regions for research in a two-dimensional array form, finding the position of an iris in each region by using a face 68 key point extraction and detection method, and stipulating an iris position range according to region division to obtain students with highconcentration; meanwhile, the ratio of the number of students meeting the two conditions of class head raising and high concentration to the number of all students is the required class head raisingrate, so that the class quality is detected at the completion rate; the face recognition and iris positioning method under the CNN model is utilized to solve the problems that students raise their heads but do not listen to the class and the face cannot be recognized due to different positions of the students, and the class quality is detected more accurately.

Description

technical field [0001] The invention relates to a method for detecting the quality of listening to lectures, in particular to a method for detecting the quality of listening to lectures based on face recognition. Background technique [0002] In recent years, the development of artificial intelligence has penetrated into many aspects of educational life, such as fingerprint check-in, iris recognition, face recognition and other technologies have been widely used in the classroom attendance system. Behavior provides a powerful tool. The research on the head-up rate of students in the classroom based on face recognition proposed by the present invention analyzes the efficiency of students' lectures by detecting the number of people who listen carefully to the class, provides a basis for classroom evaluation, and realizes more targeted teaching. [0003] Convolutional Neural Network (CNN) is a deep neural network with a convolutional structure. The convolutional structure can ...

AI Technical Summary

Problems solved by technology

Compared with the fully connected neural network of DNN, the input of CNN is an image, which utilizes the local information of the image through parameter sharing and local perception to extract image features through convolution and pooling, which greatly reduces the parameters that the neural network needs to train. did not reduce accuracy

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