Application of FLDA Based on Weighting Multiple Kernel Learning in Face Recognition
Wang Yudon · Video Engineering · 2014
Recent applications and researches of pattern recognition show that single kernel has been maturely applied in face recognition.However,classification efficiency of single kernel is not excellent.To address this problem,a multiple kernel construction method named Fisher Linear Discriminative Analysis based on Weighting Multiple Kernel Learning(WMKL-FLDA) is proposed in this paper.The constructed kernel is a linear combination of several base kernels with a constraint on their weights.By maximizing the margin maximization criterion(MMC),it presents an iterative scheme for weight optimization.The experiments on the FERET and CMU PIE face databases show that proposed multiple kernel learning method achieves high recognition performance comparing with single-kernel-based FDA and the constructed kernel relaxes parameter selection for kernel-based FLDA to some extent.