Merging Subspace Models for Face Recognition
The merging problem for principal subspace (PS) models is considered in the form: given two principal subspace models \(\mathcal M_i\) for independent training data sequences, assuming that the original data is not available, find the subspace model for the union of the original data sets. The principal subspace merging (PSM) algorithm and its approximated version (APSM) are proposed to solve the problem. The accuracy and the complexity of the approach has been mathematically analyzed and verified on face image models. If data vectors are modeled by projections into a linear subspace of dimension r in N dimensional feature space then the algorithm has O(r(4N 2+13r 2)) time complexity.
KeywordsFace Recognition Singular Value Decomposition Projection Error Singular Subspace Descriptor Size
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