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Probabilistic Visual Learning for Object Representation
July 1997 (vol. 19 no. 7)
pp. 696-710

Abstract—We present an unsupervised technique for visual learning, which is based on density estimation in high-dimensional spaces using an eigenspace decomposition. Two types of density estimates are derived for modeling the training data: a multivariate Gaussian (for unimodal distributions) and a Mixture-of-Gaussians model (for multimodal distributions). These probability densities are then used to formulate a maximum-likelihood estimation framework for visual search and target detection for automatic object recognition and coding. Our learning technique is applied to the probabilistic visual modeling, detection, recognition, and coding of human faces and nonrigid objects, such as hands.

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Index Terms:
Face recognition, gesture recognition, target detection, subspace methods, maximum-likelihood, density estimation, principal component analysis, Eigenfaces.
Citation:
Baback Moghaddam, Alex Pentland, "Probabilistic Visual Learning for Object Representation," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp. 696-710, July 1997, doi:10.1109/34.598227
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