Low Resolution Facial Image Recognition via Multiple Kernel Criterion
Practical face recognition systems are sometimes confronted with low-resolution (LR) images. Most existing feature extraction algorithms aim to preserve relational structure among objects of the input space in a linear embedding space. However, it has been a consensus that such complex visual learning tasks will be well be solved by adopting multiple descriptors to more precisely characterize the data for improving performance. In this paper, we addresses the problem of matching LR and highresolution images that are difficult for conventional methods in practice due to the lack of an efficient similarity measure, and a multiple kernel criterion (MKC) is proposed for LR face recognition without any super-resolution (SR) preprocessing. Different image descriptors including RsL2, LBP, Gradientface and IMED are considered as the multiple kernel generators and the Gaussian function is exploited as the distance induced kernel. MKC solves this problem by minimizing the inconsistency between the similarities captured by the multiple kernels, and the nonlinear objective function can be alternatively minimized by a constrained eigenvalue decomposition. Experiments on benchmark databases show that our MKC method indeed improves the recognition performance.
Chuan-Xian Ren Dao-Qing Dai Hong Yan
Center of Computer Vision and Department of MathematicsSun Yat-Sen UniversityGuangzhou, China 510275 Department of Electric Engineering, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon, Hong
国际会议
北京
英文
204-208
2011-11-28（万方平台首次上网日期，不代表论文的发表时间）