title: PEM-PCA: a parallel expectation-maximization PCA face recognition architecture.
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.
authors: Applied Network Technology (ANT) Laboratory, Department of Computer Science, Faculty of Science, Khon Kaen University, Khon Kaen, Thailand.
authors: Kanokmon
authors: K
authors: Rujirakul
authors: Applied Network Technology (ANT) Laboratory, Department of Computer Science, Faculty of Science, Khon Kaen University, Khon Kaen, Thailand.
authors: Chakchai
authors: 0000-0003-1026-191X
authors: C
authors: So-In
authors: Department of Mathematics, Faculty of Science, Khon Kaen University, Khon Kaen, Thailand.
authors: Banchar
authors: B
authors: Arnonkijpanich
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D005145:Face; D006801:Humans; D010363:Pattern Recognition, Automated; D025341:Principal Component Analysis