Abstract—Principal Component Analysis (PCA) is a statistical technique used for dimension reduction and recognition, & widely used for facial feature extraction and recognition. In this paper a cluster based SPCA face recognition method has been proposed. Experiments based on ORL face database have performed to compare the recognition rate between tradition PCA, Advanced principal component analysis (APCA), & SPCA. It is found that SPCA is giving the best classification result.
Index Terms—Security, Biometrics, Face Recognition, Principal Component Analysis, Eigenspace
Arjun V Mane is with the Department of Computer Science and Information Technology, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad-431 004 (MS) INDIA, (telephone: +91-9923616649, e-mail: arjunmane7113@yahoo.co.in).
Ramesh R. Manza, is with the Department of Computer Science andInformation Technology, Dr. Babasaheb Ambedkar Marathwada University,Aurangabad-431 004 (MS) INDIA, (telephone: +91-9421308853, e-mail: ramesh_manza@yahoo.com).
Karbhari V Kale, is with the Department of Computer Science and Information Technology, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad-431 004 (MS) INDIA, (telephone: +91-9422203089, e-mail: kvkale91@gmail.com).
Cite: Arjun V. MANE, Ramesh R. MANZA and Karbhari V KALE, "Human Face Recognition Using Superior Principal Component Analysis (SPCA)," International Journal of Computer Theory and Engineering vol. 2, no. 5, pp. 688-691, 2010.
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