Facial identification of twins based on fusion score method

Abstract

Face recognition system contains lots of challenges due to various environmental factors, background variations, poor quality of camera, different illumination and others. Since twins are involved with criminal activities, twin identification becomes an essential task. The proposed system is focused on identifying the identical twins for the still images. The fusion based approach has been implemented in the proposed system. It combines the features extracted by using Principal Component Analysis (PCA), Histogram Oriented of Gradients (HOG), Local Binary Pattern (LBP), Gabor and distance between the facial components. Three types of fusion such as Decision Level Fusion, Feature Level Fusion and Score Level Fusion are used in the proposed approach. Based on these fusion generated scores, the twin has been identified. In the proposed system, Particle Swarm Optimization is used for the best feature selection and SVM classifier is used for training and testing the image. The proposed system provides better results when compared with the other twin detection techniques.