Study On Learning Concentration Recognition Method For Distance Education

With the popularization of Internet technology,Long-distance network learning methods are also more and more popular.Due to the special nature of the network distance education,the limitation of time and space has been broken through.Distance learners can choose the content they want to learn anytime,anywhere.At present,the application fields of network distance education are also getting wider and wider,and the ways are also becoming more and more diversified.Network distance education really provides a good resource platform for our modern education.However,at the same time,there are also many problems in the network distance education.Due to the time and space constraints of learners and teachers,students and teachers can not communicate face to face,which often leads to fatigue and tiredness of learners for a long time in the face of computer and mobile phone screens Emotions,resulting in decreased concentration,Even affect the physical and mental health of students.The change of emotional status of online learners directly affects the learning efficiency,so the emotional problems of remote online learners are important issues that we should pay attention to.We can judge students 'learning status through affective recognition,and then take effective measures to improve or avoid this phenomenon.However,the development of facial expression recognition technology provides a good technical support for identifying learners' emotional problems.The widespread application of facial expression recognition technology is conducive to harmonious human-computer interaction,so that remote network education can exert its maximum effect,improve the lack of distance education,improve the learning efficiency of learners and make the distance education more intelligent and humane.This paper firstly elaborates the research background and significance of network distance education,analyzes the learner's emotion problem,and then analyzes and compares some common face recognition algorithms.In this paper,we propose a face recognition algorithm based on Haar feature that is good for positive face recognition,and analyze the Adaboost algorithm in detail,and introduce the face training process and classification training recognition effect.Through the analysis of the facial expression changes in the process of learner learning,we find that the eyes and mouth are the most obvious expression features in the learning process.In view of the beneficial effect of Adaboost algorithm,this paper still trains the eye and the eye separately by Adaboost algorithm Mouth classifier,and achieved a good recognition training effect.Because of the peculiarity of distance learning network,we analyzed three aspects of learning-related expressions: focus,fatigue and normal,by analyzing the changes of facial features of online learners.We also analyzed the specific facial features.According to the established facial expression model,facial features of online learners are extracted,including the features of eyes and mouth,Then the method of fuzzy reasoning is used to make fuzzy data of the characteristic data,Finally,a comprehensive decision method is used to determine the learning state of the network learners,and an effective rule base is formulated.The experimental results show that the methods used in this paper can correctly classify the learning state of the learners.