| Abstract |
Principal component analysis or PCA has been traditionally used as one of the feature extraction techniques in face recognition
systems yielding high accuracy when requiring a small number of features. However, the covariance matrix and eigenvalue
decomposition stages cause high computational complexity, especially for a large database. Thus, this research presents an
alternative approach utilizing an Expectation-Maximization algorithm to reduce the determinant matrix manipulation resulting in
the reduction of the stages’ complexity. To improve the computational time, a novel parallel architecture was employed to utilize the
benefits of parallelization of matrix computation during feature extraction and classification stages including parallel preprocessing,
and their combinations, so-called a Parallel Expectation-Maximization PCA architecture. Comparing to a traditional PCA and its
derivatives, the results indicate lower complexity with an insignificant difference in recognition precision leading to high speed face
recognition systems, that is, the speed-up over nine and three times over PCA and Parallel PCA. |
Publication
Journal Publication
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