Palmprint and face multi-modal biometric recognition based on SDA-GSVD and its kernelization
| Autor: | Sheng Li, Yongfang Yao, Chao Lan, Xiao-Yuan Jing, Jingyu Yang, Wenqian Li, Jiasen Lu |
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| Rok vydání: | 2012 |
| Předmět: |
Biometry
Biometrics Computer science generalized singular value decomposition (GSVD) Feature extraction kernel subclass discriminant analysis (KSDA) lcsh:Chemical technology Biochemistry Kernel principal component analysis Article Analytical Chemistry Discriminative model Humans lcsh:TP1-1185 Electrical and Electronic Engineering palmprint and face Instrumentation Principal Component Analysis business.industry subclass discriminant analysis (SDA) Discriminant Analysis Pattern recognition Linear discriminant analysis Hand Atomic and Molecular Physics and Optics Multimodal biometrics Kernel (statistics) Kernelization Face Principal component analysis Artificial intelligence Generalized singular value decomposition business Algorithms multimodal biometric feature extraction |
| Zdroj: | Sensors (Basel, Switzerland) Sensors Volume 12 Issue 5 Pages 5551-5571 Sensors, Vol 12, Iss 5, Pp 5551-5571 (2012) |
| ISSN: | 1424-8220 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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