NDLI: Abstracted radon profiles for fingerprint recognition

Content Provider	IEEE Xplore Digital Library
Author	Sandhan, T. Hyung Jin Chang Jin Young Choi
Copyright Year	2013
Description	Author affiliation: Sch. of Electr. Eng. & Comput. Sci., Seoul Nat. Univ., Seoul, South Korea (Sandhan, T.; Hyung Jin Chang; Jin Young Choi)
Abstract	Conventional minutiae-based fingerprint recognition approaches consider only local characteristics and their accuracy dramatically decreases as the number of available minutiae decreases. We propose new features based on Abstracted Radon Profile (ARP). Proposed method uses global properties of an image and it does not necessitate any heavy preprocessing as in classical methods. By using independent gradual patching via proposed multilayer architecture, local characteristics of an image are also preserved. ARP features have an advantage of being robust to zero mean additive noise. For sparse signal representation, dictionary is constructed from the ARP features of the training samples. Recognition is done by $ℓ_{1}-minimization$ with quadratic constraints, so this framework can handle dense noise by exploiting the fact that these errors are often sparse. Experimental results in assessing recognition performance demonstrate the proposed approach outperforms the conventional approaches in correlation and distance based comparisons. Computational time comparison result shows the proposed feature is more efficient than brute-force method of image alignment and promising for handling other pattern recognition problems as well.
Starting Page	4156
Ending Page	4160
File Size	1516622
Page Count	5
File Format	PDF
ISBN	9781479923410
DOI	10.1109/ICIP.2013.6738856
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-09-15
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Abstracted Radon Profile Pattern-matching fingerprint sparse representation
Content Type	Text
Resource Type	Article

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