Robust palm print and knuckle print recognition system using a contactless approach

Goh Kah Ong Michael, Tee Connie, Beng Jin Teoh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

19 Citations (Scopus)

Abstract

This paper proposes an innovative contactless palm print and knuckle print recognition system. We present a novel palm print and knuckle print tracking approach to automatically detect and capture these features from low resolution video stream. No constraint is imposed and the subject can place his/her hand naturally on top of the sensor without touching any device. Besides, we introduce a simple yet robust directional coding technique to encode the palm print feature in bit string representation. In addition, a new scheme is presented to extract knuckle print feature using ridgelet transform. Our method is different from the others in the sense that we do not resize the knuckle print images to standard size. The geometrical information of the knuckle print can thus be retained and utilized in our work. Support Vector Machine was used to fuse the scores output by the palm print and knuckle print experts. The fusion of these features yields promising result for real-time application.

Original languageEnglish
Title of host publicationProceedings of the 2010 5th IEEE Conference on Industrial Electronics and Applications, ICIEA 2010
Pages323-329
Number of pages7
DOIs
Publication statusPublished - 2010 Sep 1
Event5th IEEE Conference on Industrial Electronics and Applications, ICIEA 2010 - Taichung, Taiwan, Province of China
Duration: 2010 Jun 152010 Jun 17

Publication series

NameProceedings of the 2010 5th IEEE Conference on Industrial Electronics and Applications, ICIEA 2010

Other

Other5th IEEE Conference on Industrial Electronics and Applications, ICIEA 2010
CountryTaiwan, Province of China
CityTaichung
Period10/6/1510/6/17

Fingerprint

Electric fuses
Support vector machines
Fusion reactions
Sensors

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

Cite this

Michael, G. K. O., Connie, T., & Teoh, B. J. (2010). Robust palm print and knuckle print recognition system using a contactless approach. In Proceedings of the 2010 5th IEEE Conference on Industrial Electronics and Applications, ICIEA 2010 (pp. 323-329). [5516864] (Proceedings of the 2010 5th IEEE Conference on Industrial Electronics and Applications, ICIEA 2010). https://doi.org/10.1109/ICIEA.2010.5516864
Michael, Goh Kah Ong ; Connie, Tee ; Teoh, Beng Jin. / Robust palm print and knuckle print recognition system using a contactless approach. Proceedings of the 2010 5th IEEE Conference on Industrial Electronics and Applications, ICIEA 2010. 2010. pp. 323-329 (Proceedings of the 2010 5th IEEE Conference on Industrial Electronics and Applications, ICIEA 2010).
@inproceedings{91f3ccd7a4cd49929764fcc7704cda4c,
title = "Robust palm print and knuckle print recognition system using a contactless approach",
abstract = "This paper proposes an innovative contactless palm print and knuckle print recognition system. We present a novel palm print and knuckle print tracking approach to automatically detect and capture these features from low resolution video stream. No constraint is imposed and the subject can place his/her hand naturally on top of the sensor without touching any device. Besides, we introduce a simple yet robust directional coding technique to encode the palm print feature in bit string representation. In addition, a new scheme is presented to extract knuckle print feature using ridgelet transform. Our method is different from the others in the sense that we do not resize the knuckle print images to standard size. The geometrical information of the knuckle print can thus be retained and utilized in our work. Support Vector Machine was used to fuse the scores output by the palm print and knuckle print experts. The fusion of these features yields promising result for real-time application.",
author = "Michael, {Goh Kah Ong} and Tee Connie and Teoh, {Beng Jin}",
year = "2010",
month = "9",
day = "1",
doi = "10.1109/ICIEA.2010.5516864",
language = "English",
isbn = "9781424450466",
series = "Proceedings of the 2010 5th IEEE Conference on Industrial Electronics and Applications, ICIEA 2010",
pages = "323--329",
booktitle = "Proceedings of the 2010 5th IEEE Conference on Industrial Electronics and Applications, ICIEA 2010",

}

Michael, GKO, Connie, T & Teoh, BJ 2010, Robust palm print and knuckle print recognition system using a contactless approach. in Proceedings of the 2010 5th IEEE Conference on Industrial Electronics and Applications, ICIEA 2010., 5516864, Proceedings of the 2010 5th IEEE Conference on Industrial Electronics and Applications, ICIEA 2010, pp. 323-329, 5th IEEE Conference on Industrial Electronics and Applications, ICIEA 2010, Taichung, Taiwan, Province of China, 10/6/15. https://doi.org/10.1109/ICIEA.2010.5516864

Robust palm print and knuckle print recognition system using a contactless approach. / Michael, Goh Kah Ong; Connie, Tee; Teoh, Beng Jin.

Proceedings of the 2010 5th IEEE Conference on Industrial Electronics and Applications, ICIEA 2010. 2010. p. 323-329 5516864 (Proceedings of the 2010 5th IEEE Conference on Industrial Electronics and Applications, ICIEA 2010).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - Robust palm print and knuckle print recognition system using a contactless approach

AU - Michael, Goh Kah Ong

AU - Connie, Tee

AU - Teoh, Beng Jin

PY - 2010/9/1

Y1 - 2010/9/1

N2 - This paper proposes an innovative contactless palm print and knuckle print recognition system. We present a novel palm print and knuckle print tracking approach to automatically detect and capture these features from low resolution video stream. No constraint is imposed and the subject can place his/her hand naturally on top of the sensor without touching any device. Besides, we introduce a simple yet robust directional coding technique to encode the palm print feature in bit string representation. In addition, a new scheme is presented to extract knuckle print feature using ridgelet transform. Our method is different from the others in the sense that we do not resize the knuckle print images to standard size. The geometrical information of the knuckle print can thus be retained and utilized in our work. Support Vector Machine was used to fuse the scores output by the palm print and knuckle print experts. The fusion of these features yields promising result for real-time application.

AB - This paper proposes an innovative contactless palm print and knuckle print recognition system. We present a novel palm print and knuckle print tracking approach to automatically detect and capture these features from low resolution video stream. No constraint is imposed and the subject can place his/her hand naturally on top of the sensor without touching any device. Besides, we introduce a simple yet robust directional coding technique to encode the palm print feature in bit string representation. In addition, a new scheme is presented to extract knuckle print feature using ridgelet transform. Our method is different from the others in the sense that we do not resize the knuckle print images to standard size. The geometrical information of the knuckle print can thus be retained and utilized in our work. Support Vector Machine was used to fuse the scores output by the palm print and knuckle print experts. The fusion of these features yields promising result for real-time application.

UR - http://www.scopus.com/inward/record.url?scp=77956043899&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=77956043899&partnerID=8YFLogxK

U2 - 10.1109/ICIEA.2010.5516864

DO - 10.1109/ICIEA.2010.5516864

M3 - Conference contribution

AN - SCOPUS:77956043899

SN - 9781424450466

T3 - Proceedings of the 2010 5th IEEE Conference on Industrial Electronics and Applications, ICIEA 2010

SP - 323

EP - 329

BT - Proceedings of the 2010 5th IEEE Conference on Industrial Electronics and Applications, ICIEA 2010

ER -

Michael GKO, Connie T, Teoh BJ. Robust palm print and knuckle print recognition system using a contactless approach. In Proceedings of the 2010 5th IEEE Conference on Industrial Electronics and Applications, ICIEA 2010. 2010. p. 323-329. 5516864. (Proceedings of the 2010 5th IEEE Conference on Industrial Electronics and Applications, ICIEA 2010). https://doi.org/10.1109/ICIEA.2010.5516864