The kernel function is introduced to solve the nonlinear pattern recognition problem. The advantage of a kernel method often depends critically on a proper choice of the kernel function. A promising approach is to learn the kernel from data automatically. Over the past few years, some methods which have been proposed to learn the kernel have some limitations: learning the parameters of some prespecified kernel function and so on. In this paper, the nonlinear face verification via learning the kernel matrix is proposed. A new criterion is used in the new algorithm to avoid inverting the possibly singular within-class which is a computational problem. The experimental results obtained on the facial database XM2VTS using the Lausanne protocol show that the verification performance of the new method is superior to that of the primary method Client Specific Kernel Discriminant Analysis (CSKDA). The method CSKDA needs to choose a proper kernel function through many experiments, while the new method could learn the kernel from data automatically which could save a lot of time and have the robust performance.

中文翻译：

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通过学习核矩阵进行人脸验证

引入核函数来解决非线性模式识别问题。核方法的优势通常取决于核函数的正确选择。一种有前景的方法是自动从数据中学习内核。在过去的几年里，一些被提出来学习核的方法有一些局限性：学习一些预先指定的核函数的参数等等。在本文中，提出了通过学习核矩阵的非线性人脸验证。在新算法中使用了新准则以避免反转可能是计算问题的类内奇异。使用洛桑协议在人脸数据库 XM2VTS 上获得的实验结果表明，新方法的验证性能优于主要方法 Client Specific Kernel Discriminant Analysis (CSKDA)。CSKDA方法需要通过多次实验选择合适的核函数，而新方法可以自动从数据中学习核，可以节省大量时间并具有鲁棒性。