Department of Computer Science
Kernel machine-based rank-lifting regularized discriminant analysis method for face recognition
To address two problems, namely nonlinear problem and singularity problem, of linear discriminant analysis (LDA) approach in face recognition, this paper proposes a novel kernel machine-based rank-lifting regularized discriminant analysis (KRLRDA) method. A rank-lifting theorem is first proven using linear algebraic theory. Combining the rank-lifting strategy with three-to-one regularization technique, the complete regularized methodology is developed on the within-class scatter matrix. The proposed regularized scheme not only adjusts the projection directions but tunes their corresponding weights as well. Moreover, it is shown that the final regularized within-class scatter matrix approaches to the original one as the regularized parameter tends to zero. Two public available databases, namely FERET and CMU PIE face databases, are selected for evaluations. Compared with some existing kernel-based LDA methods, the proposed KRLRDA approach gives superior performance. © 2011 Elsevier B.V.
Face recognition, Kernel method, Nonlinear problem, Rank-lifting scheme, Singularity problem
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Chen, W., Yuen, P., & Xie, X. (2011). Kernel machine-based rank-lifting regularized discriminant analysis method for face recognition. Neurocomputing, 74 (17), 2953-2960. https://doi.org/10.1016/j.neucom.2011.04.019