Classifying Facial Expression Using Support Vector Machine Based on Bidirectional Local Binary Pattern Histogram Feature Descriptor

Abstract

Human gestures pertaining to face are a usual way for humans to describe their feelings and emotions. This research aims to develop a facial recognition model and apply it to the current set of expressions. There are five senses: fear, pleasure, disgust, sadness and surprise using the innovative vector machine (SVM) and the local binary (LBP). In this work, we propose a new method of recognition of facial expression that uses a local binary pattern (LBP) and a local phase quantification (LPQ) based on Gabor's facial image. To capture the outstanding visual properties, the Gabor filter is first adopted to extract the characteristics of the face image between five scales and eight orientations. Then, the Gabor image is encoded by the LBP operator and the LPQ operator, respectively. The proposed algorithm is then compared with other algorithm viz: principal component analysis. The result shows that the proposed method outperforms many other approaches in this document in terms of accuracy and using Japanese Female Facial Expression Database.