Sample awareness-based personalized facial expression recognition

Abstract

The behavior of the current emotion classification model to recognize all test samples using the same method contradicts the cognition of human beings in the real world, who dynamically change the methods they use based on current test samples. To address this contradiction, this study proposes an individualized emotion recognition method based on context awareness. For a given test sample, a classifier that was deemed the most suitable for the current test sample was first selected from a set of candidate classifiers and then used to realize the individualized emotion recognition. The Bayesian learning method was applied to select the optimal classifier and then evaluate each candidate classifier from the global perspective to guarantee the optimality of each candidate classifier. The results of the study validated the effectiveness of the proposed method.