Abstract
Human hand possesses some of the most distinctive anatomical features which have been extensively used for the biometrics identification. However there are several hand features which results from complex interaction among bones, muscles, skin and tissues (therefore these are expected to be anatomically unique to individuals) which remain relatively unexplored for their potential in biometrics especially for forensic applications. This paper explores the possibility of using lowest finger knuckle patterns formed on the joints between metacarpal and the proximal phalanx bones for the biometrics identification. We automatically segment such region of interest from the hand images and normalize/enhance them to accommodate illumination, scale and pose variations resulting from the contactless imaging. The normalized knuckle images are used to match using several matchers popular in the literature. We use database of 110 different subjects acquired from the contactless hand imaging to ascertain the performance. We also evaluate the performance from matching of such lowest finger knuckle patterns using two session data acquired after an interval of at least two years. The experimental results are very encouraging and demonstrate potential of such unexplored finger knuckle patterns for the biometrics applications.
Original language | English |
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Title of host publication | Proceedings - 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2014 |
Publisher | IEEE Computer Society |
Pages | 106-112 |
Number of pages | 7 |
ISBN (Electronic) | 9781479943098 |
DOIs | |
Publication status | Published - 1 Jan 2014 |
Event | 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2014 - Columbus, United States Duration: 23 Jun 2014 → 28 Jun 2014 |
Conference
Conference | 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2014 |
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Country/Territory | United States |
City | Columbus |
Period | 23/06/14 → 28/06/14 |
Keywords
- Finger Dorsal Biometrics
- Finger Knuckle Identification
- Hand Biometrics
- Minor Finger Knuckle Recognitiong
ASJC Scopus subject areas
- Computer Vision and Pattern Recognition
- Electrical and Electronic Engineering