Attention Detection Method Based On Multi-feature Fusion And Its Application In E-learning

With the rapid development of computer technology and network,E-learning teaching mode is no longer limited by time and space,thus achieving efficient learning anytime and anywhere.However,the current E-learning teaching mode generally has some shortcomings,such as waste of resources,low learning efficiency,etc,and it's key to solve these problems by improved human-computer interaction experience.According to the characteristics of the E-learning environment,this paper proposes a multi-feature fusion attention detection method to improve the human-computer interaction experience,which includes faces detection,eyes open or closed state detection,focus of sight on screen detection and emotional recognition,the main work includes:(1)Based on least square ellipse fitting algorithm,an improved method for human eye state detection is proposed.For the problem that the least squares ellipse fitting algorithm cannot accurately fit the contours of human eyes,we introduce the idea of finding the optimal solution,to eliminate the influence of interferences such as glasses and accurately fit the contours.At the same time,the ellipse is adjusted according to the deviation between the fitting ellipse and the actual contour of human eyes,to obtain the actual width and height of eyes.Compared with similar methods,the proposed method effectively improves the detection accuracy.(2)An improved algorithm for focus of sight on screen detection is proposed.The algorithm reduces the computation and improves the detection accuracy by narrowing the detection area of the iris center.At the same time,we introduce ellipse fitting algorithm to reduce the influence of eyelids on the detection.Compared with similar methods,this method achieves higher accuracy.(3)Aiming at the problem of poor human-computer interaction experience,an emotional recognition method based on fuzzy reasoning is proposed.The method takes the upper and lower radians of mouth angle and the proportion of forehead texture as the input of fuzzy system,and uses the minimum ambiguity method to obtain the membership function.Then we establish the rule base according to prior knowledge and distribution of membership function.Finally,the emotional categories are output through fuzzy reasoning.Experimental results verify the effectiveness of the proposed method.(4)The attention detection module is integrated to implement the attention detection system.This paper integrates each feature module into a whole solution and develops a complete attention detection system,which analyses the real-time dynamic feedback detection results of each module in a comprehensive way.The experimental results show that compared with the single feature attention detection method,the proposed scheme not only achieves significant improvement in both effectiveness and accuracy,but also has good man-machine interaction at the same time.