Authors : P. Latha , S. Annadurai , L. Ganesan and Allwin Jefred
Abstract: The task of facial recognition is discriminating input signals (image Data) into several classes (persons). In this study two algorithms Eigen analysis and Laplacian analysis of face recognition are implemented and compared. These methods differ in the kind of projection method been used and in the similarity matching criterion employed. Eigen analysis uses Eigen Vectors , Principal Component analysis and Weight Vector for the recognition of input facial image. In Lapalacian method Locality Preserving Projections (LPP) are used in which the input face images are mapped into a face subspace for analysis. Different from Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) which effectively see only the Euclidean structure of face space, LPP finds an embedding that preserves local information and obtains a face subspace that best detects the essential face manifold structure. In this study , we compare the Laplacian face approach with Eigen face methods on 25 different face data sets. Experimental results suggest that the Laplacian face approach provides a better representation and achieves lower error rates in face recognition.
P. Latha , S. Annadurai , L. Ganesan and Allwin Jefred , 2007. Comparison of Face Recognition Using Eigen Analysis and Laplacian Analysis . Asian Journal of Information Technology, 6: 943-947.