Biometric feature extraction using local fractal auto-correlation

doi:10.1088/1674-1056/23/9/096401

Abstract Image texture feature extraction is a classical means for biometric recognition. To extract effective texture feature for matching, we utilize local fractal auto-correlation to construct an effective image texture descriptor. Three main steps are involved in the proposed scheme: (i) using two-dimensional Gabor filter to extract the texture features of biometric images; (ⅱ) calculating the local fractal dimension of Gabor feature under different orientations and scales using fractal auto-correlation algorithm; and (ⅲ) linking the local fractal dimension of Gabor feature under different orientations and scales into a big vector for matching. Experiments and analyses show our proposed scheme is an efficient biometric feature extraction approach.

Keywords: fractal auto-correlation fractal dimension Gabor filter biometric recognition

Received: 10 October 2013
Revised: 23 February 2014
Accepted manuscript online:

PACS:	64.60.al	(Fractal and multifractal systems)
	42.30.-d	(Imaging and optical processing)
	42.30.Sy	(Pattern recognition)

Fund: Project supported by the National Natural Science Foundation of China (Grant Nos. 61262040, 61271341, 81360230, and 61271007) and the Applied Basic Research Projects of Yunnan Province, China (Grant No. KKSY201203062).

Corresponding Authors: Chen Xi
E-mail: xcbiometrics@126.com

Cite this article:

Chen Xi (陈熙), Zhang Jia-Shu (张家树) Biometric feature extraction using local fractal auto-correlation 2014 Chin. Phys. B 23 096401

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