关键词: Gabor滤波器, 不确定度, Haar特征, 二维主成分分析, 嵌入式系统, 人脸识别

Abstract: Gabor uncertainty features fusion can solve the problem that multiscale Gabor features are unsuitable for ARM because of huge data and dimensions in the embedded face recognition system. Multiscale Gabor features were first extracted, and then the uncertain weight was calculated, at last multiscale Gabor features were integrated into one. The embedded face recognition system detected face by using Haar-like features of face, and reduced dimensions by using 2-Dimensional Principal Component Analysis (2DPCA) algorithm. Based on EELiod 270 development board, the performance of face recognition was tested on ORL and Yale. Comparative results with other face recognition algorithms show that a significant decline is got in the amount of arithmetic operations, and a good real-time recognition is obtained while ensuring the recognition rate.

Key words: Gabor filter, uncertainty, Haar feature, 2-Dimensional Principal Component Analysis (2DPCA), embedded system, face recognition