Dynamic training using multistage clustering for face recognition
Pattern Recognition
Two-dimensional maximum margin feature extraction for face recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on cybernetics and cognitive informatics
C++ implementation of neural networks trainer
INES'09 Proceedings of the IEEE 13th international conference on Intelligent Engineering Systems
Efficient and reliable training of neural networks
HSI'09 Proceedings of the 2nd conference on Human System Interactions
Two-dimensional heteroscedastic discriminant analysis for facial gender classification
ICIC'09 Proceedings of the 5th international conference on Emerging intelligent computing technology and applications
Facial expression recognition using two-class discriminant features
BioID_MultiComm'09 Proceedings of the 2009 joint COST 2101 and 2102 international conference on Biometric ID management and multimodal communication
Improved computation for Levenberg-Marquardt training
IEEE Transactions on Neural Networks
Short communication: Diagnosis of bladder cancers with small sample size via feature selection
Expert Systems with Applications: An International Journal
Entropy-based iterative face classification
BioID'11 Proceedings of the COST 2101 European conference on Biometrics and ID management
Multi-view human movement recognition based on fuzzy distances and linear discriminant analysis
Computer Vision and Image Understanding
Recognition of driving postures by multiwavelet transform and multilayer perceptron classifier
Engineering Applications of Artificial Intelligence
Exploiting fisher and fukunaga-koontz transforms in chernoff dimensionality reduction
ACM Transactions on Knowledge Discovery from Data (TKDD)
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A novel algorithm that can be used to boost the performance of face-verification methods that utilize Fisher's criterion is presented and evaluated. The algorithm is applied to similarity, or matching error, data and provides a general solution for overcoming the "small sample size" (SSS) problem, where the lack of sufficient training samples causes improper estimation of a linear separation hyperplane between the classes. Two independent phases constitute the proposed method. Initially, a set of weighted piecewise discriminant hyperplanes are used in order to provide a more accurate discriminant decision than the one produced by the traditional linear discriminant analysis (LDA) methodology. The expected classification ability of this method is investigated throughout a series of simulations. The second phase defines proper combinations for person-specific similarity scores and describes an outlier removal process that further enhances the classification ability. The proposed technique has been tested on the M2VTS and XM2VTS frontal face databases. Experimental results indicate that the proposed framework greatly improves the face-verification performance