Rujirakul Kanokmon, So-In Chakchai, Arnonkijpanich Banchar
Applied Network Technology (ANT) Laboratory, Department of Computer Science, Faculty of Science, Khon Kaen University, Khon Kaen, Thailand.
Department of Mathematics, Faculty of Science, Khon Kaen University, Khon Kaen, Thailand.
ScientificWorldJournal. 2014;2014:468176. doi: 10.1155/2014/468176. Epub 2014 Apr 15.
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.
主成分分析（PCA）传统上一直被用作人脸识别系统中的特征提取技术之一，在需要少量特征时能产生较高的准确率。然而，协方差矩阵和特征值分解阶段会导致高计算复杂度，尤其是对于大型数据库而言。因此，本研究提出了一种替代方法，利用期望最大化算法来减少行列式矩阵运算，从而降低这些阶段的复杂度。为了缩短计算时间，采用了一种新颖的并行架构，以利用在特征提取和分类阶段（包括并行预处理及其组合）进行矩阵计算并行化的优势，即所谓的并行期望最大化主成分分析（Parallel Expectation-Maximization PCA）架构。与传统主成分分析及其衍生方法相比，结果表明复杂度更低，而识别精度的差异不显著，从而实现了高速人脸识别系统，即在速度上比主成分分析和并行主成分分析分别快九倍和三倍以上。
