论文标题
通过学习内核矩阵面对验证
Face Verification via learning the kernel matrix
论文作者
论文摘要
引入内核函数以解决非线性模式识别问题。内核方法的优势通常取决于核函数的正确选择。一种有希望的方法是自动从数据中学习内核。在过去的几年中，有人提出了一些学习内核的方法有一些局限性：学习某些预先指定的内核函数的参数等。在本文中，提出了通过学习核基质的非线性面部验证。在新算法中使用了一个新标准，以避免将可能的奇异课堂颠倒，这是一个计算问题。使用Lausanne协议在面部数据库XM2VT上获得的实验结果表明，新方法的验证性能优于主要方法客户端特定核心判别分析（CSKDA）。 CSKDA方法需要通过许多实验选择适当的内核函数，而新方法可以自动从数据中学习内核，从而可以节省大量时间并具有强大的性能。
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.
