Facial expression recognition using binary pattern and embedded hidden Markov model

This paper proposes a robust facial expression recognition approach using an enhanced center-symmetric local binary pattern (ECS-LBP) and embedded hidden Markov model (EHMM). The ECS-LBP operator encodes the texture information of a local face region by emphasizing diagonal components of a previous center-symmetric local binary pattern (CS-LBP). Here, the diagonal components are emphasized because facial textures along the diagonal direction contain much more information than those of other directions. Generally, feature extraction and categorization for facial expression recognition are the most key issue. To address this issue, we propose a method to combine ECS-LBP and EHMM, which is the key contribution of this paper. The performance evaluation of proposed method was performed with the CK facial expression database and the JAFFE database, and the proposed method showed performance improvements of 2.65% and 2.19% compared to conventional method using two-dimensional discrete cosine transform (2D-DCT) and EHMM for CK database and JAFFE database, respectively. Through the experimental results, we confirmed that the proposed approach is effective for facial expression recognition.