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Please use this identifier to cite or link to this item: http://ir.ncue.edu.tw/ir/handle/987654321/18381

Title: Finger-Vein Pattern Identification Using SVM and Neural Network Technique
Authors: Wu, Jian-Da;Liu, Chiung-Tsiung
Contributors: 車輛科技研究所
Keywords: Finger-vein pattern identification;Support vector machine;Neural network;Vehicle safety system
Date: 2011-10
Issue Date: 2014-04-29T07:28:40Z
Publisher: Elsevier Ltd
Abstract: This paper presents a support vector machine (SVM) technique for finger-vein pattern identification in a personal identification system. Finger-vein pattern identification is one of the most secure and convenient techniques for personal identification. In the proposed system, the finger-vein pattern is captured by infrared LED and a CCD camera because the vein pattern is not easily observed in visible light. The proposed verification system consists of image pre-processing and pattern classification. In the work, principal component analysis (PCA) and linear discriminant analysis (LDA) are applied to the image pre-processing as dimension reduction and feature extraction. For pattern classification, this system used an SVM and adaptive neuro-fuzzy inference system (ANFIS). The PCA method is used to remove noise residing in the discarded dimensions and retain the main feature by LDA. The features are then used in pattern classification and identification. The accuracy of classification using SVM is 98% and only takes 0.015 s. The result shows a superior performance to the artificial neural network of ANFIS in the proposed system
Relation: Expert Systems with Applications, 38(11): 14284-14289
Appears in Collections:[車輛科技研究所] 期刊論文

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