PEM-PCA: A Parallel Expectation-Maximization PCA Face Recognition Architecture

• Main Authors: Kanokmon Rujirakul, Chakchai So-In, Banchar Arnonkijpanich
• Format: Article
• Language: English
• Published: Hindawi Limited 2014-01-01
• Series: The Scientific World Journal
• Online Access: http://dx.doi.org/10.1155/2014/468176

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.