NDLI: Automatically Face Detection and Recognition System Based on Principal Component Analysis ( PCA ) with Back Propagation Neural Networks ( BPNN )

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Automatically Face Detection and Recognition System Based on Principal Component Analysis ( PCA ) with Back Propagation Neural Networks ( BPNN )

Content Provider	Semantic Scholar
Author	Kashem, Mohammod Abul Ahmed, Shamim Akhter, Nasim Alam, Mahbub
Copyright Year	2011
Abstract	Face recognition has received substantial attention from researches in biometrics, pattern recognition field and computer vision communities. Face recognition can be applied in Security measure at Air ports, Passport verification, Criminals list verification in police department, Visa processing , Verification of Electoral identification and Card Security measure at ATM's. In this paper, a face recognition system for personal identification and verification using Principal Component Analysis (PCA) with Back Propagation Neural Networks (BPNN) is proposed. This system consists on three basic parts, first: the Face Detection partwhich automatically detect human face image using BPNN, second: the various facial features extraction, and the third: face recognition are performed based on Principal Component Analysis (PCA) with BPNN. The dimensionality of face image is reduced by the PCA and the recognition is done by the BPNN for efficient and robust face recognition. This paper also focuses the face database with different sources of variations, especially pose, expression, accessories, lighting and bacgrounds would be used to advance the state-of-the-art face recognition technologies aiming at practical applications
File Format	PDF HTM / HTML
Alternate Webpage(s)	http://www.ijcit.org/ijcit_papers/vol2no1/IJCIT-110734.pdf
Language	English
Access Restriction	Open
Content Type	Text
Resource Type	Article

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