A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
A Method of Combining Multiple Experts for the Recognition of Unconstrained Handwritten Numerals
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern classification: a unified view of statistical and neural approaches
Pattern classification: a unified view of statistical and neural approaches
FVC2000: Fingerprint Verification Competition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Comparison of visible and infra-red imagery for face recognition
FG '96 Proceedings of the 2nd International Conference on Automatic Face and Gesture Recognition (FG '96)
Face recognition with visible and thermal infrared imagery
Computer Vision and Image Understanding - Special issue on Face recognition
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Benchmarking a Reduced Multivariate Polynomial Pattern Classifier
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recent advances in visual and infrared face recognition: a review
Computer Vision and Image Understanding
Handbook of Face Recognition
IR and visible light face recognition
Computer Vision and Image Understanding
Score normalization in multimodal biometric systems
Pattern Recognition
How do correlation and variance of base-experts affect fusion in biometric authentication tasks?
IEEE Transactions on Signal Processing
Fusion of face and speech data for person identity verification
IEEE Transactions on Neural Networks
Fusion of visual and infra-red face scores by weighted power series
Pattern Recognition Letters
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This paper addresses the face verification problem by fusing visual and infra-red face verification systems. Unlike the conventional least squares error minimization approach which involves fitting of a learning model to data density and then perform a threshold process for error counting, this work directly formulates the required target error count rate in terms of design model parameters. A simple power series model is adopted as the fusion classifier and our experiments show promising results.