Principal Component Analysis for Face Recognition
Saurabh P. Bahurupi1, D. S. Chaudhari2
1Saurabh P.Bahurupi Department of Electronics and Telecommunication Government College of Engineering Maharashtra, India.
2D.S.Chaudhari Department of Electronics and Telecommunication Government College of Engineering Maharashtra, India.
Manuscript received on may 27, 2012. | Revised Manuscript received on June 22, 2012. | Manuscript published on June 30, 2012. | PP: 91-94 | Volume-1 Issue-5, June 2012 | Retrieval Number: E0419051512/2012©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Face recognition is a biometric technology with a wide range of potential applications such as access control, banking, information security, human computer interaction, virtual reality, database retrieval etc. This paper addresses the building of face recognition system by using Principal Component Analysis (PCA) method. PCA is a statistical approach used for reducing the number of variables in face recognition. While extracting the most relevant information (feature) contained in the images (face). In PCA, every image in the training set can be represented as a linear combination of weighted eigenvectors called as “Eigenfaces”. These eigenvectors are obtained from covariance matrix of a training image set called as basis function. The weights are found out after selecting a set of most relevant Eigenfaces. Recognition is performed by projecting a new image (test image) onto the subspace spanned by the eigenfaces and then classification is done by distance measure methods such as Euclidean distance. A number of experiments were done to evaluate the performance of the face recognition system.
Keywords: Face Recognition, Principle Component Analysis (PCA), Eigenface, Covariance matrix, Face database.