Microexpression recognition model based on non negative matrix decomposition in intelligent classroom

Abstract

At present, intelligent systems with the function of automatic micro expression recognition are gradually applied in intelligent classrooms, but there is still a problem of low recognition rate. Therefore, based on the dual graph regularization, the research constructs a joint non negative matrix decomposition algorithm model for micro expression recognition, and verifies its effectiveness. The experimental results showed that the research algorithm had the highest performance in both micro and macro expression (micro) databases, at 75.4 %. In algorithm comparison, the recognition rates of algorithm L and algorithm M in different databases were higher than those of group A and B algorithms. Among them, Algorithm 6 in Group A had the highest recognition rate in all three databases, with the highest being 57.4 %; Algorithm 12 in Group B had the highest recognition rate in Zhongke Microexpression 2 and micro and macro expression databases, with 61.1 % and 59.4 %; Algorithm 11 had the highest recognition rate in self generated micro expression databases, with 54.0 %. And algorithm L and algorithm M had a minimum of 58.5 % and a maximum of 75.4 %. In parameter sensitivity analysis, the recognition rates of parameters η, χ, and γ in all databases showed good recognition results within a certain range of values, but they would decrease when they exceeded a specific value. Overall, the algorithm model proposed in the study has high effectiveness in improving the recognition rate of micro expressions, which is significant for the micro expression recognition of students in practical intelligent classrooms.