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Palmprint and Face Multi-Modal Biometric Recognition Based on SDA-GSVD and Its Kernelization
State Key Laboratory of Software Engineering, Wuhan University, Wuhan 430072, China
College of Automation, Nanjing University of Posts and Telecommunications, Nanjing 210046, China
State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210046, China
College of Computer Science, Nanjing University of Science and Technology, Nanjing 210094, China
* Author to whom correspondence should be addressed.
Received: 1 March 2012; in revised form: 2 April 2012 / Accepted: 25 April 2012 / Published: 30 April 2012
Abstract: When extracting discriminative features from multimodal data, current methods rarely concern themselves with the data distribution. In this paper, we present an assumption that is consistent with the viewpoint of discrimination, that is, a person’s overall biometric data should be regarded as one class in the input space, and his different biometric data can form different Gaussians distributions, i.e., different subclasses. Hence, we propose a novel multimodal feature extraction and recognition approach based on subclass discriminant analysis (SDA). Specifically, one person’s different bio-data are treated as different subclasses of one class, and a transformed space is calculated, where the difference among subclasses belonging to different persons is maximized, and the difference within each subclass is minimized. Then, the obtained multimodal features are used for classification. Two solutions are presented to overcome the singularity problem encountered in calculation, which are using PCA preprocessing, and employing the generalized singular value decomposition (GSVD) technique, respectively. Further, we provide nonlinear extensions of SDA based multimodal feature extraction, that is, the feature fusion based on KPCA-SDA and KSDA-GSVD. In KPCA-SDA, we first apply Kernel PCA on each single modal before performing SDA. While in KSDA-GSVD, we directly perform Kernel SDA to fuse multimodal data by applying GSVD to avoid the singular problem. For simplicity two typical types of biometric data are considered in this paper, i.e., palmprint data and face data. Compared with several representative multimodal biometrics recognition methods, experimental results show that our approaches outperform related multimodal recognition methods and KSDA-GSVD achieves the best recognition performance.
Keywords: multimodal biometric feature extraction; palmprint and face; subclass discriminant analysis (SDA); generalized singular value decomposition (GSVD); kernel subclass discriminant analysis (KSDA)
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Cite This Article
MDPI and ACS Style
Jing, X.-Y.; Li, S.; Li, W.-Q.; Yao, Y.-F.; Lan, C.; Lu, J.-S.; Yang, J.-Y. Palmprint and Face Multi-Modal Biometric Recognition Based on SDA-GSVD and Its Kernelization. Sensors 2012, 12, 5551-5571.
Jing X-Y, Li S, Li W-Q, Yao Y-F, Lan C, Lu J-S, Yang J-Y. Palmprint and Face Multi-Modal Biometric Recognition Based on SDA-GSVD and Its Kernelization. Sensors. 2012; 12(5):5551-5571.
Jing, Xiao-Yuan; Li, Sheng; Li, Wen-Qian; Yao, Yong-Fang; Lan, Chao; Lu, Jia-Sen; Yang, Jing-Yu. 2012. "Palmprint and Face Multi-Modal Biometric Recognition Based on SDA-GSVD and Its Kernelization." Sensors 12, no. 5: 5551-5571.