Zhang Taiping, Tang Yuan Yan, Fang Bin, Shang Zhaowei, Liu Xiaoyu
IEEE Trans Image Process. 2009 Nov;18(11):2599-606. doi: 10.1109/TIP.2009.2028255. Epub 2009 Jul 24.
In this correspondence, we propose a novel method to extract illumination insensitive features for face recognition under varying lighting called the Gradientfaces. Theoretical analysis shows Gradientfaces is an illumination insensitive measure, and robust to different illumination, including uncontrolled, natural lighting. In addition, Gradientfaces is derived from the image gradient domain such that it can discover underlying inherent structure of face images since the gradient domain explicitly considers the relationships between neighboring pixel points. Therefore, Gradientfaces has more discriminating power than the illumination insensitive measure extracted from the pixel domain. Recognition rates of 99.83% achieved on PIE database of 68 subjects, 98.96% achieved on Yale B of ten subjects, and 95.61% achieved on Outdoor database of 132 subjects under uncontrolled natural lighting conditions show that Gradientfaces is an effective method for face recognition under varying illumination. Furthermore, the experimental results on Yale database validate that Gradientfaces is also insensitive to image noise and object artifacts (such as facial expressions).
在本通信中，我们提出了一种名为Gradientfaces的新方法，用于在变化光照下提取光照不敏感特征以进行人脸识别。理论分析表明，Gradientfaces是一种光照不敏感度量，对包括非受控自然光照在内的不同光照具有鲁棒性。此外，Gradientfaces源自图像梯度域，由于梯度域明确考虑了相邻像素点之间的关系，因此它能够发现人脸图像的潜在固有结构。因此，Gradientfaces比从像素域提取的光照不敏感度量具有更强的区分能力。在68个受试者的PIE数据库上达到99.83%的识别率，在10个受试者的Yale B数据库上达到98.96%的识别率，以及在132个受试者的室外数据库上在非受控自然光照条件下达到95.61%的识别率，表明Gradientfaces是一种在变化光照下进行人脸识别的有效方法。此外，在Yale数据库上的实验结果验证了Gradientfaces对图像噪声和对象伪像（如面部表情）也不敏感。
