Finger-vein pattern identification using principal component analysis and the neural network technique |
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| Authors: | Jian-Da Wu Chiung-Tsiung Liu |
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| Affiliation: | 1. School of Computer Science and Technology, Shandong University of Finance and Economics, Jinan 250014, PR China;2. The Shandong Province Key Laboratoty of Digital Media Technology, Shandong University of Finance and Economics, Jinan 250014, PR China;3. School of Computer Science and Technology, Shandong University, Jinan 250101, PR China;1. Department of Informatics, University of Science and Technologies Mohamed-Boudiaf Oran, Algeria;2. Centro Algoritmi, School of Engineering, University of Minho, Campus of Azurém, 4800-058 Guimarães, Portugal;3. Department of Engineering Technology, Miami University, Hamilton, OH 45011, USA;1. Division of Electronic and Information Engineering, Chonbuk National University, Jeonju 561-756, Republic of Korea;2. College of Machinery and Automation, Wuhan University of Science and Technology, Wuhan 430081, China;3. School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China;4. Department of Business and Computer Science, Southwestern Oklahoma State University, OK 73096, USA;5. Department of Computer Engineering, Mokpo National University, Jeonnam 534-729, Republic of Korea;1. School of Computer Science and Technology, Shandong University, Jinan 250101, PR China;2. School of Computer Science and Technology, Shandong University of Finance and Economics, Jinan 250014, PR China;1. Silesian University of Technology, Faculty of Automatic Control, Electronics and Computer Science, ul. Akademicka 16, 44-100 Gliwice, Poland;2. Silesian University of Technology, Institute of Electronics, Akademicka 16, 44-100, Gliwice, Poland |
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| Abstract: | This paper presents a personal identification system using finger-vein patterns with component analysis and neural network technology. In the proposed system, the finger-vein patterns are captured by a device that can transmit near infrared through the finger and record the patterns for signal analysis. The proposed biometric system for verification consists of a combination of feature extraction using principal component analysis (PCA) and pattern classification using back-propagation (BP) network and adaptive neuro-fuzzy inference system (ANFIS). Finger-vein features are first extracted by PCA method to reduce the computational burden and removes noise residing in the discarded dimensions. The features are then used in pattern classification and identification. To verify the effect of the proposed ANFIS in the pattern classification, the BP network is compared with the proposed system. The experimental results indicated the proposed system using ANFIS has better performance than the BP network for personal identification using the finger-vein patterns. |
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