Class attending quality detection method based on face recognition

A quality inspection method and face recognition technology, which is applied in the field of lecture quality inspection, can solve the problems of reducing accuracy, reducing accuracy, and achieving the effects of reducing accuracy, reducing costs, and reducing workload

Inactive Publication Date: 2020-11-10
HARBIN ENG UNIV
View PDF7 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Class attending quality detection method based on face recognition
  • Class attending quality detection method based on face recognition
  • Class attending quality detection method based on face recognition

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0020] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0021] Classroom head-up rate is one of the tools to effectively measure the efficiency and quality of students' classroom lectures. The present invention is based on deep learning image processing and face recognition technology. By intercepting the video stream of classroom lectures with a specified number of frames, and then performing preliminary calculations for face recognition heads up rate. However, since looking up does not reflect the quality of the students' lectures, some students may glance at other places, so we also need to analyze and study the students' concentration. Because the difference in the location of the students will lead to the different ranges of the irises of their eyes, this requires us to divide all the students into regions, so as to judge the efficiency and quality of the students' lectures. Finally,...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

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 ...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G06K9/00G06Q50/20G06N3/04
CPCG06Q50/205G06V40/165G06V40/171G06V40/19G06N3/045
Inventor 杨丽宏张珺勃柴睿鸽
Owner HARBIN ENG UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap