关键词: 全局方差, 局部方差, 核最大方差差分嵌入, 生物特征提取

Abstract: Maximum Variance Difference Embedding（VDE） maximizes the difference between the global variance and the local variance, which utilizes the maximum variance difference criterion rather than the generalized Rayleigh quotient criterion as a class separability measure, thereby avoiding the singularity problem of Unsupervised Discriminant Projection（UDP） when addressing the sample size problem. However, VDE is a linear approach, which cannot describe the complex nonlinear data, such as biometric data. To enhance the nonlinear description ability of VDE, it can optimize the objective function of VDE in reproducing kernel Hilbert space to construct Kernel-based maximum Variance Difference Embedding（KVDE） approach. Compared with some other related classical methods, experimental results on face and palmprint recognition problems indicate the effectiveness of the proposed KVDE.

Key words: global variance, local variance, kernel maximum variance difference embedding, biometric feature extraction