Digital Image Processing
Pattern Recognition Letters - In memory of Professor E.S. Gelsema
Palmprint recognition using eigenpalms features
Pattern Recognition Letters
Fisherpalms based palmprint recognition
Pattern Recognition Letters
Machine Vision and Applications
A Biometric Identification System Based on Eigenpalm and Eigenfinger Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
Personal recognition based on an image of the palmar surface of the hand
Pattern Recognition
Minutiae feature analysis for infrared hand vein pattern biometrics
Pattern Recognition
Review article: Touch-less palm print biometrics: Novel design and implementation
Image and Vision Computing
A multi-matcher system based on knuckle-based features
Neural Computing and Applications
On selecting Gabor features for biometric authentication
International Journal of Computer Applications in Technology
Image and Vision Computing
An innovative contactless palm print and knuckle print recognition system
Pattern Recognition Letters
Identity verification through palm vein and crease texture
ICB'06 Proceedings of the 2006 international conference on Advances in Biometrics
Personal identification using knuckleprint
SINOBIOMETRICS'04 Proceedings of the 5th Chinese conference on Advances in Biometric Person Authentication
An introduction to biometric recognition
IEEE Transactions on Circuits and Systems for Video Technology
Biometric verification using thermal images of palm-dorsa vein patterns
IEEE Transactions on Circuits and Systems for Video Technology
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This paper describes a hand-based biometric system by using visible and infrared imagery. We develop an acquisition device which could capture both color and infrared hand images. We modify an ordinary web camera to capture the hand vein that normally requires specialized infrared sensor. Our design is simple and low-cost, and we do not need additional installation of special apparatus. The device can capture the epidermal and subcutaneous features from the hand simultaneously. In specific, we acquire four independent, yet complementary features namely palm print, knuckle print, palm vein, and finger vein, from the hand for recognition. As a low-resolution sensor is deployed in this study, the images quality may be slightly poorer than those acquired using high resolution scanner or CCD camera. The line and ridge patterns on the hand may not appear clear. Therefore, we propose a pre-processing technique to enhance the contrast and sharpness of the images so that the dominant print and line features can be highlighted and become disguisable from the background. After that, we use a simple feature extractor called Directional Coding to obtain useful representation of the hand modalities. The hand features are fused using Support Vector Machine (SVM). The fusion of these features yields promising result for practical multi-modal biometrics system.