Scaling up Class-Specific Kernel Discriminant Analysis for large-scale Face Verification
Research output: Contribution to journal › Article › Scientific › peer-review
|Journal||IEEE Transactions on Information Forensics and Security|
|Publication status||Published - 2016|
|Publication type||A1 Journal article-refereed|
In this paper, a novel approximate solution of the criterion used in non-linear class-specific discriminant subspace learning is proposed. We build on the Class-Specific Kernel Spectral Regression method which is a two-step process formed by an eigenanalysis step and a kernel regression step. Based on the structure of the intra-class and out-of-class scatter matrices, we provide a fast solution for the first step. For the second step, we propose the use of approximate kernel space definitions. We analytically show that the adoption of randomized and classspecific kernels have the effect of regularization and Nystr¨ombased approximation, respectively. We evaluate the proposed approach in face verification problems and compare it with existing approaches. Experimental results show the effectiveness and efficiency of the proposed Approximate Class-Specific Kernel Spectral Regression method, since it can provide satisfactory performance and scale well with the size of the data.