Lu Juwei, Plataniotis K N, Venetsanopoulos A N
Dept. of Electr. and Comput. Eng., Toronto Univ., Ont., Canada.
IEEE Trans Neural Netw. 2003;14(1):117-26. doi: 10.1109/TNN.2002.806629.
Techniques that can introduce low-dimensional feature representation with enhanced discriminatory power is of paramount importance in face recognition (FR) systems. It is well known that the distribution of face images, under a perceivable variation in viewpoint, illumination or facial expression, is highly nonlinear and complex. It is, therefore, not surprising that linear techniques, such as those based on principle component analysis (PCA) or linear discriminant analysis (LDA), cannot provide reliable and robust solutions to those FR problems with complex face variations. In this paper, we propose a kernel machine-based discriminant analysis method, which deals with the nonlinearity of the face patterns' distribution. The proposed method also effectively solves the so-called "small sample size" (SSS) problem, which exists in most FR tasks. The new algorithm has been tested, in terms of classification error rate performance, on the multiview UMIST face database. Results indicate that the proposed methodology is able to achieve excellent performance with only a very small set of features being used, and its error rate is approximately 34% and 48% of those of two other commonly used kernel FR approaches, the kernel-PCA (KPCA) and the generalized discriminant analysis (GDA), respectively.
在人脸识别（FR）系统中，能够引入具有增强判别能力的低维特征表示的技术至关重要。众所周知，在可感知的视角、光照或面部表情变化下，人脸图像的分布是高度非线性且复杂的。因此，诸如基于主成分分析（PCA）或线性判别分析（LDA）的线性技术无法为那些存在复杂人脸变化的FR问题提供可靠且稳健的解决方案，这也就不足为奇了。在本文中，我们提出了一种基于核机器的判别分析方法，该方法可处理人脸模式分布的非线性问题。所提出的方法还有效解决了大多数FR任务中存在的所谓“小样本量”（SSS）问题。新算法已在多视角UMIST人脸数据库上，就分类错误率性能进行了测试。结果表明，所提出的方法仅使用极少的一组特征就能实现出色的性能，其错误率分别约为另外两种常用的核FR方法——核主成分分析（KPCA）和广义判别分析（GDA）的34%和48%。
