Facial expression recognition based on Weighted All Parts Accumulation and Optimal Expression-specific Parts Accumulation

With the increasing applications of human computer interactive systems, recognizing accurate and application oriented human expressions is becoming a challenging topic. The face is highly attractive biometric trait for expression recognition because of its physiological structure, its robustness and location. In this paper we proposed modified subspace projection method that is an extension of our previous work [11]. Our previous work was FER analysis on full face and half faces by using principal component analysis (PCA) for feature extraction. This is obviously an extension of existing PCA algorithm. In this paper PCA is applied on facial parts like left eye, right eye, nose and mouth for feature extraction. A Flow chart for the whole system is depicted in section 3. The objective of this research is to develop a more effective approach to distinguish between seven prototypic facial expressions, such as neutral, smile, anger, surprise, fear, disgust, and sadness. These techniques clearly outperform our previous paper[11]. The whole procedure is applied on Cohn-kanade FEA dataset and we achieved higher accuracy than our previous method.