标题：REAL-TIME FACE DETECTION AND RECOGNITION USING PRINCIPAL COMPONENT ANALYSIS (PCA) � BACK PROPAGATION NEURAL NETWORK (BPNN) AND RADIAL BASIS FUNCTION (RBF)
期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2016
卷号：91
期号：1
出版社：Journal of Theoretical and Applied
摘要：Face Recognition is one of the most important and fastest growing biometric area during the last several years and become the most successful application in image processing and broadly used in security systems. A real-time system for recognizing faces using mobile device or webcam was implemented. Face detection is the first basic step of any face recognition system. Viola-Jones method is used to detect and crop face area from the image. Feature extraction considered as a main challenge in any face recognition system. Principal Component Analysis (PCA) is efficient and used for feature extraction and dimension reduction. Back Propagation Neural Network (BPNN) and Radial Basis Function (RBF) are used for classification process. RBF is considered the result of BPNN output layer as input. The system is tested and achieve high recognition rates. Information about individuals was stored in a database.
