  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.
