A discriminated correlation classifier for face recognition
Proceedings of the 2010 ACM Symposium on Applied Computing
Enhancing Clustering Quality through Landmark-Based Dimensionality Reduction
ACM Transactions on Knowledge Discovery from Data (TKDD)
Gait-based human age estimation
IEEE Transactions on Information Forensics and Security
Proceedings of the 4th International Conference on Uniquitous Information Management and Communication
Multilinear nonparametric feature analysis
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part II
Explicit integration of identity information from skin regions to improve face recognition
ICIAR'12 Proceedings of the 9th international conference on Image Analysis and Recognition - Volume Part II
Nearest neighbor classifier generalization through spatially constrained filters
Pattern Recognition
Non-parametric Fisher's discriminant analysis with kernels for data classification
Pattern Recognition Letters
Computers in Biology and Medicine
Selective generation of Gabor features for fast face recognition on mobile devices
Pattern Recognition Letters
Regularized discriminant entropy analysis
Pattern Recognition
Face recognition using scale-adaptive directional and textural features
Pattern Recognition
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In this paper, we develop a new framework for face recognition based on nonparametric discriminant analysis (NDA) and multi-classifier integration. Traditional LDA-based methods suffer a fundamental limitation originating from the parametric nature of scatter matrices, which are based on the Gaussian distribution assumption. The performance of these methods notably degrades when the actual distribution is Non-Gaussian. To address this problem, we propose a new formulation of scatter matrices to extend the two-class nonparametric discriminant analysis to multi-class cases. Then, we develop two more improved multi-class NDA-based algorithms (NSA and NFA) with each one having two complementary methods based on the principal space and the null space of the intra-class scatter matrix respectively. Comparing to the NSA, the NFA is more effective in the utilization of the classification boundary information. In order to exploit the complementary nature of the two kinds of NFA (PNFA and NNFA), we finally develop a dual NFA-based multi-classifier fusion framework by employing the over complete Gabor representation to boost the recognition performance. We show the improvements of the developed new algorithms over the traditional subspace methods through comparative experiments on two challenging face databases, Purdue AR database and XM2VTS database.