Feature-adaptive motion energy analysis for facial expression recognition

Abstract

In this paper, we present a facial expression recognition method using feature-adaptive motion energy analysis. Our method is simplicity-oriented and avoids complicated face model representations or computationally expensive algorithms to estimate facial motions. Instead, the proposed method uses a simplified action-based face model to reduce the computational complexity of the entire facial expression analysis and recognition process. Feature-adaptive motion energy analysis estimates facial motions in a costeffective manner by assigning more computational complexity on selected discriminative facial features. Facial motion intensity and orientation evaluation are then performed accordingly. Both facial motion intensity and orientation evaluation are based on simple calculations by exploiting a few motion energy values in the difference image, or optimizing the characteristics of featureadaptive facial feature regions. For facial expression classification, a computationally inexpensive decision tree is used since the information gain heuristics of ID3 decision tree forces the classification to be done with minimal Boolean comparisons. The feasibility of the proposed method is shown through the experimental results as the proposed method recognized every facial expression in the JAFFE database by up to 75% with very low computational complexity.