基于增强Gabor特征和直接分步线性判别分析的人脸识别

Gabor特征能从不同方向和尺度有效表示人脸图片的局部特征,但是利用传统Gabor特征的方法却忽略原始人脸图片所包含的全局特征。文中把Gabor特征和原始图片信息结合起来,构成增强的Gabor特征,并结合直接分步线性判别分析算法,提出一种人脸识别方法。在Yale、ORL和Georgia Tech人脸库的仿真实验结果表明,相对于传统Gabor特征,增强Gabor特征能够有效提高人脸识别率。

Face Recognition Based on Enhanced Gabor Feature and Direct Fractional-Step Linear Discriminant Analysis

Gabor features can effectively represent the local features of face image with different directions and scales. However, traditional Gabor features based algorithms neglect the global features of the original image. Enhanced Gabor features (EGF) is developed in this paper by combining Gabor features and information extracted from the original image. A face recognition method is further proposed based on EGF and direct fractional-step linear discriminant analysis algorithm (DF_LDA). Experiment results of simulation on Yale, ORL and Georgia face databases show that EGF can effectively improve the face recognition rate compared with the traditional Gabor features.