Finger-vein pattern identification using principal component analysis and the neural network technique

Authors:
Jian-Da Wu;Chiung-Tsiung Liu
Affiliations:
Graduate Institute of Vehicle Engineering, National Changhua University of Education, 1 Jin-De Rd., Changhua City, Changhua 500, Taiwan;Graduate Institute of Vehicle Engineering, National Changhua University of Education, 1 Jin-De Rd., Changhua City, Changhua 500, Taiwan
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

Citing 7
Cited 1

Application of the Karhunen-Loeve Procedure for the Characterization of Human Faces

IEEE Transactions on Pattern Analysis and Machine Intelligence
Biometric identification systems

Signal Processing
Distance measures for PCA-based face recognition

Pattern Recognition Letters
Novel approaches to principal component analysis of image data based on feature partitioning framework

Pattern Recognition Letters
Eigenfeature Regularization and Extraction in Face Recognition

IEEE Transactions on Pattern Analysis and Machine Intelligence
Driver identification using finger-vein patterns with Radon transform and neural network

Expert Systems with Applications: An International Journal
Face recognition with radial basis function (RBF) neural networks

IEEE Transactions on Neural Networks

A novel matching strategy for finger vein recognition

IScIDE'12 Proceedings of the third Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering

Quantified Score

Hi-index	12.05

Visualization

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.