Face Recognition Case Study

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Abstract Face is a complex multidimensional structure and needs good computing techniques for recognition. Our approach treats face recognition as a two-dimensional recognition problem. In this scheme face recognition is done by Principal Component Analysis (PCA). Face images are projected onto a face space that encodes best variation among known face images. The face space is defined by Eigen face which are eigenvectors of the set of faces, which may not correspond to general facial features such as eyes, nose and lips. The Eigen face approach uses the PCA for recognition of the images. The system performs by projecting pre extracted face image onto a set of face space that represents significant variations among known face images. Face will…show more content…
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.2 Face Recognition . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Chapter-2 4 2 Literature Survey 5 2.1 Principal Component Analysis (PCA) . . . . . . . . . . . . . . . . . 5 2.2 Eigen Face Approach . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.2.1 Eigen Values and Eigen Vectors . . . . . . . . . . . . . . . . 7 2.2.2 Face Image Representation . . . . . . . . . . . . . . . . . . . 7 2.2.3 Mean and Mean Centered Images . . . . . . . . . . . . . . . 8 2.2.4 Covariance Matrix . . . . . . . . . . . . . . . . . . . . . . . 8 2.2.5 Eigen Face Space . . . . . . . . . . . . . . . . . . . . . . . . 9 v 2.3 Recognition Step . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 Chapter-3 11 3 Implementation…show more content…
. . . . . . . . . . . . . . . . . . . . . . . . . . 12 3.1.1 Training Set . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 3.1.2 Testing Conditions . . . . . . . . . . . . . . . . . . . . . . . 14 3.2 Face Recognition Using Eigen Faces . . . . . . . . . . . . . . . . . . 14 3.2.1 Face Image Testing . . . . . . . . . . . . . . . . . . . . . . . 14 3.2.2 Mean Face . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 3.2.3 Eigen Face . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 Chapter-4 18 4 Result 19 4.1 Result and Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 4.1.1 E_ciency . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 Chapter-5 21 5 Conclusion 22 5.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 Bibliography 23 List of Figures 3.1 A colored face image . . . . . . . . . . . . . . . . . . . . . . . . . . 12 3.2 Grey scale face image . . . . . . . . . . . . . . . . . . . . . . . . . . 13 3.3 A single face image for ten di_erent expressions . . . . . . . . . . . 13 3.4 Image in reduced light intensity . . . . . . . . . . . . . . . . . . . . 14 3.5 200 _ 200 image as input . . . . . . . . . . . . . . . . . . . . . . . .

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