Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face recognition by elastic bunch graph matching
Intelligent biometric techniques in fingerprint and face recognition
Face Recognition: Features Versus Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Comparison of Face Verification Results on the XM2VTS Database
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 4
Recognizing faces with PCA and ICA
Computer Vision and Image Understanding - Special issue on Face recognition
Robust Real-Time Face Detection
International Journal of Computer Vision
Journal of Cognitive Neuroscience
Face verification competition on the XM2VTS database
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
ICB'06 Proceedings of the 2006 international conference on Advances in Biometrics
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We propose a novel approach for matching and preservation of face images based on Adaptive Resonance Theory MAPping (ARTMAP) network. ART networks possess incrementally growing structure and provide stable on-line learning, which ensures that all patterns presented to the network, will be learned and compactly stored. Moreover the network's weights will be adapted after each classification. These characteristics are important for successful recognition of an object, which patterns are quite changeable in time. In our implementation called FaceART the network is learned from raw images as well as from eigenfaces decomposition coefficients. In order to compare the error rates of the implemented system to existing academic face recognition systems the XM2VTS database with Lausanne protocol is employed. We show that compared to the nearest neighbor rule the presented classification approach has better verification performance and more compact template representation.