Face Recognition using Gray level Co-occurrence Matrix and Snap ...
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Face Recognition using Gray level Co-occurrence Matrix and Snap ...
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ISSN: 2277-3754 ISO 9001:2008 Certified International Journal of Engineering and Innovative Technology (IJEIT) Volume 2, Issue 6, December 2012
Face Recognition using Gray level Co-occurrence Matrix and Snap Shot Method of the Eigen Face M. Madhu, R. Amutha Sri Chandrasekharendra Saraswathi Viswa Mahavidyalaya University, Kanchipuram, India SSN College of Engineering, Chennai, India expressions and expression recognition gets tougher due to the faces varying in age, gender, and ethnicity. The method presented in [1, 3, 23] applied digital curvelet co-efficient to form features for representing the entire face. In order to classify the facial expressions, the local facial information needs to be stored. To obtain the local description of the expressions, local binary patterns (LBPs) are computed using selected sub-bands of image pre-processed by curvelet transform. LBP was proposed by T. Ojala in [19] for texture classification. LBP‟s have been used extensively for expression recognition with a good rate of success in [20]. PCA is derived from Karhunen-Loeve's transformation. Given an s-dimensional vector representation of each face in a training set of images, Principal Component Analysis (PCA) tends to find a t-dimensional subspace whose basis vectors correspond to the maximum variance direction in the original image space. This new subspace is normally lower dimensional (t
