光照变化条件下人脸识别方法研究

Research on Face Recognition Method Under Uncontrolled Illumination Variation

提出了一种在变化光照条件下,具有高识别率和快速的人脸识别新算法。新算法利用韦伯局部描述算子对人脸图像进行预处理,经预处理后的图像在对光照变化具有鲁莽性,采用改进的线性判别分析算法进行特征提取,利用最近邻分类器进行分类识别。新算法分别在Yale、The Extended Yale Database B人脸库进行测试,并与一些经典的方法进行比较,实验结果显示,新算法可以获得较高的识别率,尤其是在光照变化比较大的情况下,新算法更具优势,同时,新算法的速度快,完全满足变化光照条件下的人脸识别实时性的要求。

A new face recognition algorithm is proposed with high recognition rate under uncontrolled illumination conditions. The new algorithm process face images in advance using Weber local descriptor, which means that the processed image is insensitive to illumination changing. An improved linear discriminant analysis algorithm is adopt for feature extracting, finally, nearest neighbor classifier based on Euclidean distance is applied to classify. The new algorithm is tested on Yale and The Extended Yale Database B face database respectively, in comparison with classic face recognition algorithms, the performance of the proposed method is superior to other＇s under uncontrolled illumination variation and the speed of the proposed method can fully meet the requirements of real-time face recognition.