Application of the Karhunen-Loeve Procedure for the Characterization of Human Faces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
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International Journal of Computer Vision
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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ICVS '99 Proceedings of the First International Conference on Computer Vision Systems
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CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
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IEEE Transactions on Information Theory
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Computer Vision and Image Understanding - Special issue on Face recognition
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ACM Computing Surveys (CSUR)
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Image and Vision Computing
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Computer Vision and Image Understanding
A novel statistical generative model dedicated to face recognition
Image and Vision Computing
A weighted probabilistic approach to face recognition from multiple images and video sequences
Image and Vision Computing
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PCM'10 Proceedings of the 11th Pacific Rim conference on Advances in multimedia information processing: Part I
Kernel discriminant transformation for image set-based face recognition
Pattern Recognition
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This paper introduces a new face recognition system that can be used to index (and thus retrieve) images and videos of a database of faces. New face recognition approaches are needed because, although much progress has been made to identify face taken from different viewpoints, we still cannot robustly identify faces under different illumination conditions, or when the facial expression changes, or when a part of the face is occluded on account of glasses or parts of clothing.When face recognition methods have worked in the past, it was only when all possible "image variations" were learned. Principal Components Analysis (PCA) and Fisher Discriminant Analysis (FDA) are well-known cases of such methods.In this paper we present a different approach to the indexing of face images. Our approach is based on identifying frontal faces and it allows reasonable variability in facial expressions, illumination conditions, and occlusions caused by eye-wear or items of clothing such as scarves. We divide a face image into n different regions, analyze each region with PCA, and then use a Bayesian approach to finding the best possible global match between a query image and a database image. The relationships between the n parts is modeled by using Hidden Markov Models (HMMs).