基于能量的自适应局部Gabor特征提取的人脸识别

Face recognition with adaptive local-Gabor features based on energy

为了解决传统Gabor滤波器组在人脸识别过程中特征提取时间长、计算量大的问题,从不同方向、不同尺度以及全局角度按照能量大小构建了3种不同的局部Gabor滤波器组用来提取人脸特征。首先,分析数据库中部分图像Gabor变换后的图像能量,从不同角度选出能量较大的图像构建对应的局部Gabor滤波器组;其次,根据所选滤波器组提取局部Gabor特征;然后,采用线性判别分析(LDA)法进一步提取Fisher特征;最后,利用最近邻法识别人脸图像。基于ORL人脸库和YALE人脸库的实验结果表明提出的人脸识别方法降低了人脸图像的特征维数,缩短了特征提取的时间,有效地提高了人脸识别率。

Concerning the time-consuming and computational complexity in extracting face features of traditional Gabor filters, the face features were extracted by using three different local Gabor filters adaptively chosen by the Gabor images＇ energy from different directions, scales and overall situation. Firstly, the Gabor features of some images in the face database were extracted and analyzed, and the local Gabor filters were built by choosing the filters corresponding to the images with larger energy. And then, the Fisher features were extracted using Linear Discriminate Analysis （LDA） further. Finally, face recognition was realized using the nearest neighbor method. The experimental results based on ORL and YALE face database show that the proposed approach has better face recognition performance with less feature dimension and calculation time.