2D­PCA와 영상분할을 이용한 얼굴인식

Abstract

Face recognition refers to the process of identifying individuals based on their facial features. It has recently become one of the most popular research areas in the fields of computer vision, machine learning, and pattern recognition because it spans numerous consumer applications, such as access control, surveillance, security, credit­card verification, and criminal identification. However, illumination variation on face generally cause performance degradation of face recognition systems under practical environments. Thus, this paper proposes an novel face recognition system using a fusion approach based on local binary pattern and two­dimensional principal component analysis. To minimize illumination effects, the face image undergoes the local binary pattern operation, and the resultant image are divided into two sub­images. Then, two­dimensional principal component analysis algorithm is separately applied to each sub­images. The individual scores obtained from two sub­images are integrated using a weighted­summation rule, and the fused­score is utilized to classify the unknown user. The performance evaluation of the proposed system was performed using the Yale B database and CMU­PIE database, and the proposed method shows the better recognition results in comparison with existing face recognition techniques.