Research on Finger-Knuckle-Print based Personal Authentication

Lin Zhang, School of Software Engineering, Tongji University

Lei Zhang, Dept. Computing, The Hong Kong Polytechnic University


Introduction

It has been found that the finger-knuckle-print (FKP), which refers to the inherent skin patterns of the outer surface around the phalangeal joint of one¡¯s finger, has high capability to discriminate different individuals, making it an emerging biometric identifier. In this project, we aim to develop a practical FKP-based personal authentication system. Fig. 1 shows several FKP images collected by using our developed FKP imaging device.

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(b)

(c)

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 Fig. 1: Sample FKP images acquired by the developed system. (a) and (b) are from one finger while (c) and (d) are from another finger. Images from the same finger are taken at two different sessions with an interval of 56 days.


Database

An FKP database was established using the developed FKP image acquisition device. This database is intended to be a benchmark to evaluate the performance of various FKP recognition methods, and it is publicly available. In this database, FKP images were collected from 165 volunteers, including 125 males and 40 females. Among them, 143 subjects were 20~30 years old and the others were 30 ~ 50 years old. We collected samples in two separate sessions. In each session, the subject was asked to provide 6 images for each of the left index finger, the left middle finger, the right index finger and the right middle finger. Therefore, 48 images from 4 fingers were collected from each subject. In total, the database contains 7,920 images from 660 different fingers. The average time interval between the first and the second sessions was about 25 days. The maximum and minimum time intervals were 96 days and 14 days respectively.

The database can be found at http://www.comp.polyu.edu.hk/~biometrics/FKP.htm.


Embedded FKP Recognition System

We have developed an embedded standalone FKP recognition system. Such a system does not depend on general-purpose computer anymore, and it can be used in practice readily. It uses the OMAP3530 (manufactured by Texas Instruments, USA) as the main processor. Such a system has the merits of small size, fast speed, and cost effective. A video clip showing how the system works can be found in youtube: http://www.youtube.com/watch?v=HLSlj_PkWyc or youku: http://v.youku.com/v_show/id_XMjI5NDA4MzE2.html.)


Related Publications        

  1. Lin Zhang and Hongyu Li, "Encoding local image patterns using Riesz transforms: With applications to palmprint and finger-knuckle-print recognition", Image and Vision Computing, vol. 30, no. 12, pp. 1043-1051, 2012.

  2. Lin Zhang, Lei Zhang, David Zhang, and Zhenhua Guo, "Phase congruency induced local features for finger-knuckle-print recognition", Pattern Recognition, vol. 45, no. 7, pp.2522-2531, 2012.

  3. Lin Zhang, Lei Zhang, David Zhang, and Hailong Zhu, "Ensemble of local and global information for finger-knuckle-print recognition", Pattern Recognition, vol. 44, no. 9, pp. 1990-1998, 2011.

  4. Lin Zhang, Lei Zhang, David Zhang, and Hailong Zhu, "Online finger-knuckle-print verification for personal authentication", Pattern Recognition, vol. 43, no. 7, pp. 2560-2571, 2010.


Created on: Dec.12, 2010      

Last update: Aug. 30, 2014                            

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