Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Kernel Eigenfaces vs. Kernel Fisherfaces: Face Recognition Using Kernel Methods
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Robust Face Recognition via Sparse Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
AdaBoost gabor fisher classifier for face recognition
AMFG'05 Proceedings of the Second international conference on Analysis and Modelling of Faces and Gestures
Hallucinating face by eigentransformation
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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The algorithm of 105 facial feature points localization has been proposed in [1]. In this paper, we studied the stability of these feature points in different photos of the same person, and then we presented an improved face recognition system using these facial feature points to perform face recognition and check duplicate entries in database. All of these analyses and experiments are performed on identity photographs. Experimental results show that our recognition algorithm has obvious improvement in normal face recognition application and also performances satisfactorily in finding out duplicate entries in huge face image database of more than 60,000 items.