基于多尺度韦伯脸和梯度脸的复杂光照下人脸识别研究

Face Recognition under Complex Illumination Based on Multi-scale Weber-face and Gradient-face

针对复杂光照变化影响人脸识别准确性的问题,提出了一种基于多尺度韦伯脸和梯度脸相结合的复杂光照下人脸识别方法。首先定义了能够有效描述人脸纹理结构的多尺度韦伯脸,一定程度上减弱了不同光照条件的影响;其次融合多尺度韦伯脸和梯度脸提取人脸光照不变量;最后利用SVM多类分类算法实现人脸识别。使用CMU PIE与Yale B人脸库进行验证,结果表明:提出的算法能够有效消除复杂光照变化对人脸识别的影响,即在光照极差情况下,单样本图像作为训练图像也可以有很好的识别效果,且识别率显著高于韦伯脸、多尺度韦伯脸和梯度脸。

Aiming at the problem of the effect of Complex Illumination on face recognition rates, an integrated method based multi-scale Weber-face and gradient-face is proposed. The method starts with defining the multi-scale Weber-face, which can effectively describe the face texture structure and reduce the influence of different lighting to some extent. Then multiscale face and GF are fused to extract the face illumination invariant. Finally, multi-class support vector machine is employed for face authentication. The experiments are executed both on CMU-PIE and Yale B face databases. The experimental results have indicated that the proposed method can effectively eliminate the influence of face recognition under complex illumination and the recognition rates are superior to Weber-face, multiscale Weber-face and gradient-face, even single sample images with serious light as training sample images can also work well.