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Abstract

A method of recognizing a test face given an imprecise localization and/or a partially occluded face. Using an eigenspace representation of the learning image, a subspace is computed that represents localization errors within the eigenspace. This is computed by calculating an actual localization error of the localization of the learning image. Accounting for the calculated actual localization error, all possible morphed faces are projected onto the eigenspace. For all possible faces, the subspace is modeled where all of the possible localizations of the possible faces lie using a Gaussian distribution. To accommodate for possible partial occlusions, for all possible faces, the possible faces are divided into n different local parts. Each of the n local parts are modeled using a Gaussian distribution. A global probability of the test face is computed by adding all local probabilities defined by the Gaussian distribution. The test face is recognized based on the computed subspace and/or the accounted for possible partial occlusions.

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First Claim