Facial Expression Recognition Using Local Binary Pattern and Support Vector Machine
Facial expression analysis is a remarkable and demanding problem, and impacts significant applications in various fields like human computer interaction and data driven animation. Developing an efficient facial representation from the original face images is a crucial step for achieving facial expression recognition. Facial representation based on statistical local features, Local Binary Patterns (LBP) is practically assessed. Several machine learning techniques were thoroughly observed on various databases. LBP featureswhich are effectual and competent for facial expression recognition are generally used by researchers Cohn Kanade is the database for present work and the programming language used is MATLAB. Firstly, face area is divided in small regions, by which histograms, Local Binary Patterns (LBP) are extracted and then concatenated into single feature vector. This feature vector outlines a well organized representation of face and is helpful in determining the resemblance among images. Keywords Face Recognition, Facial Expression Recognition, Local Binary Pattern, Principal Component Analysis, Support Vector Machine.