A novel iris weight map method for less constrained iris recognition based on bit stability and discriminability

Hu, Yang and Sirlantzis, Konstantinos and Howells, Gareth (2016) A novel iris weight map method for less constrained iris recognition based on bit stability and discriminability. Image and Vision Computing, 58 . pp. 168-180. ISSN 0262-8856. (doi:https://doi.org/10.1016/j.imavis.2016.05.003) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

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Official URL
http://dx.doi.org/10.1016/j.imavis.2016.05.003

Abstract

In this paper, we propose and investigate a novel iris weight map method for iris matching stage to improve less constrained iris recognition. The proposed iris weight map considers both intra-class bit stability and inter-class bit discriminability of iris codes. We model the intra-class bit stability in a stability map to improve the intra-class matching. The stability map assigns more weight to the bits that have values more consistent with their noiseless and stable estimates obtained using a low rank approximation from a set of noisy training images. Also, we express the inter-class bit discriminability in a discriminability map to enhance the inter-class separation. We calculate the discriminability map using a 1-to-N strategy, emphasizing the bits with more discriminative power in iris codes. The final iris weight map is the combination of the stability map and the discriminability map. We conduct experimental analysis on four publicly available datasets captured in varying less constrained conditions. The experimental results demonstrate that the proposed iris weight map achieves generally improved identification and verification performance compared to state-of-the-art methods.

Item Type: Article
Uncontrolled keywords: Iris recognition; Less constrained environment; Iris weight map
Subjects: T Technology > TJ Mechanical engineering and machinery > Intelligent control systems
Divisions: Faculties > Sciences > School of Engineering and Digital Arts
Faculties > Sciences > School of Engineering and Digital Arts > Image and Information Engineering
Depositing User: Konstantinos Sirlantzis
Date Deposited: 14 Dec 2015 02:47 UTC
Last Modified: 06 Mar 2017 10:09 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/53325 (The current URI for this page, for reference purposes)
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