Facial expression recognition using face clustering based discriminant analysis

This paper examines the performance of facial expression recognition improved by a discriminant analysis method. The proposed method incorporate knowledge of face clustering in linear discriminant analysis to improve it’s performance. Three face clustering approach have been considered. Intra-subject covariance and inter-subject covariance matrix are calculated in each cluster. The new sample is projected by mapping learned by samples in his cluster. Therefore, variation between train and test samples is reduced and the generalization performance of linear discriminant analysis is improved. Experimental results on the CK+ database confirm the efficiency of the proposed method in recognition rate improvement. This approach is applicable for many methods used in large scale facial expression recognition.