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Abstract: Earlier work suggests that mixture-distance can improve the performance of feature-based face recognition systems in which only a single training example is available for each individual. In this work we investigate the non-feature-based Eigenfaces technique of Turk and Pentland, replacing Euclidean distance with mixture-distance. In mixture-distance, a novel distance function is constructed based on local second-order statistics as estimated by modeling the training data with a mixture of... (Update)
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BibTeX entry: (Update)
@inproceedings{ lawrence97face,
author = "Steve Lawrence and P. Yianilos and I. Cox",
title = "Face Recognition Using Mixture-Distance and Raw Images",
booktitle = "1997 {IEEE} International Conference on Systems, Man, and Cybernetics",
publisher = "IEEE Press",
address = "Piscataway, NJ",
pages = "2016--2021",
year = "1997",
url = "citeseer.ist.psu.edu/120043.html" }
Citations (may not include all citations):338 View-based and modular eigenspaces for face recognition - Pentland, Moghaddam et al. - 1994
150 Probabilistic visual learning for object detection - Moghaddam, Pentland - 1995
98 maximum likelihood and the EM algorithm (context) - Redner, Walker - 1984
78 of Cognitive Neuroscience (context) - Turk, Pentland et al. - 1991
74 Face recognition using view-based and modular eigenspaces - Moghaddam, Pentland - 1994
26 Experiments with Eigenfaces (context) - Pentland, Starner et al. - 1993
22 Feature-based face recognition using mixture-distance - Cox, Ghosn et al. - 1996
11 Metric learning via normal mixtures - Yianilos - 1995
5 Eigenfaces code (context) - Starner - 1997
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