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access icon openaccess Fingerprint matching algorithm for poor quality images

This is an open access article published by the IET under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/)
Received 17/09/2014, Accepted 03/02/2015, Published 20/03/2015

The main aim of this study is to establish an efficient platform for fingerprint matching for low-quality images. Generally, fingerprint matching approaches use the minutiae points for authentication. However, it is not such a reliable authentication method for low-quality images. To overcome this problem, the current study proposes a fingerprint matching methodology based on normalised cross-correlation, which would improve the performance and reduce the miscalculations during authentication. It would decrease the computational complexities. The error rate of the proposed method is 5.4%, which is less than the two-dimensional (2D) dynamic programming (DP) error rate of 5.6%, while Lee's method produces 5.9% and the combined method has 6.1% error rate. Genuine accept rate at 1% false accept rate is 89.3% but at 0.1% value it is 96.7%, which is higher. The outcome of this study suggests that the proposed methodology has a low error rate with minimum computational effort as compared with existing methods such as Lee's method and 2D DP and the combined method.

Inspec keywords: image matching; fingerprint identification; dynamic programming

Other keywords: low-quality images; authentication method; poor quality images; two-dimensional dynamic programming error rate; Lee method; false accept rate; computational complexities; normalised cross-correlation; fingerprint matching algorithm; genuine accept rate; 2D DP

Subjects: Optimisation techniques; Image recognition; Optimisation techniques; Computer vision and image processing techniques

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