Facial expression recognition using ASM-based post-processing technique

Facial expression recognition is a challenging field in numerous researches, and impacts important applications in many areas such as human-computer interaction and data-driven animation, etc. Therefore, this paper proposes a facial expression recognition system using active shape model (ASM) landmark information and appearance-based classification algorithm, i.e., embedded hidden Markov model (EHMM). First, we use ASM landmark information for facial image normalization and weight factors of probability resulted from EHMM. The weight factor is calculated through investigating Kullback-Leibler (KL) divergence of best feature with high discrimination power. Next, we introduce the appearance-based recognition algorithm for classification of emotion states. Here, appearance-based recognition means the EHMM algorithm using two-dimensional discrete cosine transform (2D-DCT) feature vector. The performance evaluation of proposed method was performed with the CK facial expression database and the JAFFE database. As a result, the method using ASM information showed performance improvements of 6.5 and 2.5% compared to previous method using ASM-based face alignment for CK database and JAFFE database, respectively.