Evaluation And Evaluation System Of Students' Attentiveness Based On Machine Vision

Concentration is one of the key factors in human intelligence behavior.In recent years,we paid more attention to learners'concentration.A lot of evaluation methods of concentration have been put forward,including questionnaire survey,physiological observation and computer vision method.In the primary and secondary school,the thing parents and teachers focus on is students' score,and concentration is one of the most important factors of scores.At present,students'concentration in class is judged by teachers subjectively.On the one hand,this method distracts teachers in the class,and on the other hand,judging through naked eyes has the feature of low rate of accuracy.All these lead to the poor effect of assessing effectiveness in teaching.More importantly,judging by naked eyes cannot count and analyze students' concentration in real-time,which would result in a certain lag.In the case of rapid development of artificial intelligence technology,machine vision as a branch of artificial intelligence has always been a hot topic in academic field.The principle of machine learning is to replace the human eye with machine to obtain and judge the information.Machine vision converted the obtained information into digital image signals,using related technology of digital image processing to extract the required information.Face recognition,as one of the important topics in the field of machine vision,absorbing face images and analyzing facial features to get relevant information with machine vision.Face recognition technology has been applied to various fields,such as military,security,electronic commerce,education and so on.Aimed to the problems of students' distraction cannot be corrected in time,whether teachers' teaching has been understood or not,as well as teachers' teaching effect,students' concentration and so on,an evaluation system for students' class concentration has been designed based on machine vision.In this system,face photos can be obtained through machine vision.Specifically,a set of algorithms that can be used to detect students,class concentration has been designed based on face recognition technology,which consists of sidetrack algorithm,lift up face and low face algorithm,and eye extension algorithm.This algorithm is used to extract facial features to judge students' class concentration.Compared with the traditional concentration judgment with naked eyes,the evaluation system of students' class concentration based on machine vision has a great advantage.It improves the detection rate of face,reduce the false detection rate,and it improves the accuracy of concentration judgment to a certain extent.At the end of this thesis,we carry out experiments and analyze the evaluation system to verify the feasibility of this system.Advantages and disadvantages of the system have also been analyzed.