Score and Rank-Level Fusion for Emotion Recognition Using Genetic Algorithm

Analysis of human body movement reveals information pertinent to efficient modeling of human behavior. Identification of the most contributing features, known to perform well in recognizing emotions, is a difficult task. This article proposes to use the binary chromosome based genetic algorithm to determine a subset of features that maximizes the accuracy of four expert systems developed for emotion recognition. The approach also identifies essential features for each of the expert models by selecting the minimum subset required to maximize the recognition accuracy. Later, the expert models are fused using score-level and rank-level fusion algorithms to further improve the performance of the system. Fusion of the expert models achieved an overall emotion recognition rate of 80% on a Kinect galt database.