The nature of statistical learning theory
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Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
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A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
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FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Face Recognition Using Range Images
VSMM '97 Proceedings of the 1997 International Conference on Virtual Systems and MultiMedia
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
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Pattern Recognition Letters
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CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Face Processing: Advanced Modeling and Methods
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Estimation of Dependences Based on Empirical Data: Springer Series in Statistics (Springer Series in Statistics)
On Channel Reliability Measure Training for Multi-Camera Face Recognition
WACV '07 Proceedings of the Eighth IEEE Workshop on Applications of Computer Vision
Journal of Cognitive Neuroscience
Face detection using an SVM trained in eigenfaces space
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
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Two-dimensional face recognition suffered from pose changes, while three-dimensional approaches are with high computational complexity. Motivated by this, a two-view face recognition system for digital home is presented in this paper. Besides the improvement in recognition rate, this system reduces the misclassification that could occur in traditional single-view systems. The proposed system fuses the individual recognition results of two images of the same identity with different viewing angles based on Bayesian theory. Bayesian approach uses the similarity of each person and is trained by determining the reliability of each identity of the two channels. A frontal view and a side view are chosen since they convey the most important information of human faces. Each input image is sent into its corresponding channel to obtain a 2D face recognition result. Within each channel, PCA and SVM are applied. Different form traditional PCA based approaches, SVM classifiers are used instead of minimum distance classifier to enhance the robustness. Our experimental results show that this two-view face recognition system has achieved a higher recognition rate compared with traditional 2D single-view face recognition systems.