ǂA ǂbiometric authentication model using hand gesture images

Simon Fong (Author), Yan Zhuang (Author), Iztok Fister (Author), Iztok Fister (Author)

Abstract

A novel hand biometric authentication method based on measurements of the user's stationary hand gesture of hand sign language is proposed. The measurement of hand gestures could be sequentially acquired by a low-cost video camera. There could possibly be another level of contextual information,associated with these hand signs to be used in biometric authentication. As an analogue, instead of typing a password 'iloveu' in text which is relatively vulnerable over a communication network, a signer can encode a biometric password using a sequence of hand signs, 'i', 'l', 'o', 'v', 'e', and 'u'. Subsequently the features from the hand gesture images are extracted which are integrally fuzzy in nature, to be recognized by a classification model for telling if this signer is who he claimed himself to be, by examining over his hand shape and the postures in doing those signs. Itis believed that everybody has certain slight but unique behavioral characteristics in sign language, so are the different hand shape compositions. Simple and efficient image processing algorithms are used in hand sign recognition, including intensity profiling, color histogram and dimensionality analysis, coupled with several popular machine learning algorithms. Computer simulation is conducted for investigating the efficacy ofthis novel biometric authentication model which shows up to 93.75% recognition accuracy.

Keywords

biometric authentication;hand gesture;hand sign recognition;machine learning;

Data

Language:	English
Year of publishing:	2013
Typology:	1.01 - Original Scientific Article
Organization:	UM FERI - Faculty of Electrical Engineering and Computer Science
UDC:	004.8
COBISS:	17279254
ISSN:	1475-925X
Views:	1053
Downloads:	408
Average score:	0 (0 votes)
Metadata:

Other data

Secondary language:	Slovenian
Secondary keywords:	biometrična avtentikacija;kretnje rok;prepoznavanje kretenj;znakovni jezik;strojno učenje;
URN:	URN:SI:UM:
Type (COBISS):	Scientific work
Pages:	str. 1-26
Volume:	ǂVol. ǂ12
Issue:	ǂno. ǂ111
Chronology:	Publication date 30 Oct. 2013
DOI:	10.1186/1475-925X-12-111
ID:	10844868