Emotion detection via discriminative kernel method

Abstract

Human emotion detection is of substantial importance in diverse pervasive applications in assistive environments. Because facial expressions provide a key mechanism for understanding and conveying emotion, automatic emotion detection through facial expression recognition has attracted increased attention in both scientific research and practical applications in recent years. Traditional facial expression recognition methods normally use only one type of facial expression data, either static data extracted from one single face image or motion dependent data obtained from dynamic face image sequences, but seldom employ both. In this work, we propose a novel Discriminative Kernel Facial Emotion Recognition (DKFER) method to integrate these two types of facial expression data using a hybrid kernel, such that the advantages of both of them are exploited. In addition, by using Linear Discriminant Analysis (LDA) to transform the two types of original facial expression data into two more discriminative lower-dimensional subspaces, the succeeding classification for emotion detection can be carried out in a more efficient and effective way. Encouraging experimental results in empirical studies demonstrate the practical usage of the proposed DKFER method for emotion detection.