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

摘要： 多尺度Gabor特征的维数和数据量过大，不适合在ARM板上直接实现完成。利用计算每个尺度Gabor特征不确定度并采用加权融合的方法，很好地解决了图像维数和数据量过大的难点。加权融合过程包括多尺度Gabor特征的提取、不确定度权值的计算和加权融合过程；同时使用了类Haar特征提取人脸、利用二维主成分分析(2DPCA)对人脸图像进行降维。基于EELiod 270嵌入式开发平台，使用ORL和Yale图像库对该方法进行了测试，并与其他人脸识别算法进行比较。结果显示，在保证识别率的同时，算法运算量大幅度下降，且实时识别效果良好。

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