关键词: 人脸识别, 主成分分析, Matlab集群, 多核, 任务分割, 并行计算

Abstract: In order to take full advantage of multi-core processor resources, the parallel programming model by building blocks with multithreading was studied, hence improving the performance of the program. According to the integral structure of Principal Component Analysis (PCA)-based face recognition algorithm, a functional module named train() was designed for the training of recognizing generated samples in the environment of Matlab cluster. The parallelization of this algorithm was realized by task partition. The experimental results indicate that the stable recognition rate of 94.167% and the approximately linear speed-up ratio verify the correctness and high efficiency of the parallel algorithm.

Key words: face recognition, Principal Component Analysis (PCA), Matlab cluster, multi-core, task partition, parallel computing