Application of the Karhunen-Loeve Procedure for the Characterization of Human Faces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detecting Faces in Images: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Biometrics
Distance measures for PCA-based face recognition
Pattern Recognition Letters
Recent advances in visual and infrared face recognition: a review
Computer Vision and Image Understanding
Digital Image Processing (3rd Edition)
Digital Image Processing (3rd Edition)
Fully Automated Facial Symmetry Axis Detection in Frontal Color Images
AUTOID '05 Proceedings of the Fourth IEEE Workshop on Automatic Identification Advanced Technologies
Face recognition using multi-feature and radial basis function network
VIP '02 Selected papers from the 2002 Pan-Sydney workshop on Visualisation - Volume 22
Face recognition from a single image per person: A survey
Pattern Recognition
2D and 3D face recognition: A survey
Pattern Recognition Letters
Journal of Cognitive Neuroscience
Cell Phones Personal Authentication Systems Using Multimodal Biometrics
ICIAR '08 Proceedings of the 5th international conference on Image Analysis and Recognition
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Face recognition is the most popular non-intrusive biometric technique with numerous applications in commerce, security and surveillance. Despite its good potential, most of the face recognition methods in the literature are not practical due to the lack of robustness, slow recognition, and semi-manual localizations. In this paper, we improve the robustness of eigenface-based systems with respect to variations in illumination level, pose and background. We propose a new method for face cropping and alignment which is fully automated and we integrate this method in Eigenface algorithm for face recognition. We also investigate the effect of various preprocessing techniques and several distance metrics on the overall system performance. The evaluation of this method under single-sample and multi-sample recognition is presented. The results of our comprehensive experiments on two databases, FERET and JRFD, show a significant gain compared to basic Eigenface method and considerable improvement with respect to recognition accuracy when compared with previously reported results in the literature.