Performance Analysis of Similarity Coefficient Feature Vector on Facial Expression Recognition

Facial expression is an essential and impressive means of human contact. This is important connection of information for knowing emotional case and motive. A facial expression pursues not only emotions, but other creative action, social cooperation and psychological characteristics. Appearance based facial expression recognition systems are analyzed and have pulled widen application. A new study of bit intensity with coefficient feature vector for facial expression recognition proposed in this paper. All the binary patterns from gray color intensity values are grouped into possible number of attributes according to their similarity. Each attributes count the frequency number of similarity from binary patterns. Each image divided into equal sized blocks and extracts 4-bit binary patterns in two distinct directions for a pixel by measuring the gray color intensity values with its neighbouring pixels. For evaluation the proposed descriptors, JAFFE dataset and Support Vector Machine were applied. Proposed method has achieved excellent achievement in terms of efficiency, robustness and lessens execution time.