Emotion Estimation Algorithm from Facial Image Analyses of e-Learning Users
This paper proposes an algorithm to obtain the emotion estimation from facial images of e-Learning users. The facial expression characteristics used to distinguish emotions were obtained by examinations of the subjects. The following points were selected for the emotion analysis: A.  ・head apex ・chin B.  ・Outer Right Brow (OR_Brow) ・Inner Right Brow (IR_Brow) ・Outer Left Brow (OL_Brow) ・Inner Left Brow (IL_Brow) ・Right Lip corner (R_Lip) ・Left Lip corner (L_Lip) Table 1 shows estimated emotions of e-Learning users. 23 university students (18 male, 5 female) participated as subjects in our experiment the following data. (a) Time-sequential analysis of six normalized features point of facial images (b) Time-sequential subjective emotional self-evaluations by the subject as shown in Table 1. The positional rates of change of the facial feature points in the normalized coordinates every 30 seconds were obtained and sorted for the six emotions. Figure 1 is a matrix of rate of change. In fact, correlation Inner Right Brow(IR_Brow)(y) and Inner Left Brow(IL_Brow)(y) is high for affirmative feelings. (Correlation coefficient is 0.95)(E) Figure 2 shows standard deviation of the position change rates a function of the number of subjects. A rate of change fluctuates to about under 7. However, a rate of change converges with about 15. In addition, the same result was provided on average of rate. The procedure used to estimate the emotions is as follows. 1) Some effective features points are chosen by the emotion estimation 2) A subject is separated by two groups, as grope S and grope L 3) Selection of the eigenvector of the variance-co-variance matrices (cumulative contribution rate≧95%) 4) Emotion calculation using Mahalanobis distance. The result are shown in Fig.3 the proposed algorithm overall coincidence ratio between the computer emotion and the subjective evolution was 77.1%, while of the traditional method.