Facial Expression Recognition with LDPP & LTP using Deep Belief Network

Abstract

In this paper, local directional position pattern (LDPP) and local ternary pattern (LTP) are selected for facial recognition method which are having many advantages over previous techniques like local binary pattern (LBP) and local directional pattern (LDP). The selected techniques of LDPP and LTP are estrangement in their algorithms which help solely to extract features out of an image. LDPP is a revised form of LDP. In a typical LDP, only the top edge direction was taken into consideration, but strength sign of the pixel was not considered which may result in same code for opposite kind of edge pixel. This snag is overcome by LDPP which is further concatenated with LTP for better feature extraction. Once features are extracted they are trained using deep belief network. In the experimental work 10 images of each expression i.e. angry, surprise, disgust, neutral, sad, smile are selected. LDPP and LTP are concatenated followed by principal component analysis (PCA) and general discriminant analysis (GDA). Further for training, Deep Belief Network (DBN) is used which eventually increases the recognition rate and achieve accuracy of 95.3% which was 89.3% without concatenating.