Title: An empirical evaluation of rotation invariance of LDP feature for fingerprint matching using neural networks
Authors: Ravinder Kumar; Madasu Hanmandlu; Pravin Chandra
Addresses: Department of Computer Science and Engineering, Ansal Institute of Technology, Sector 55, Gurgaon, Haryana, India ' Department of Electrical Engineering, Indian Institute of Technology, Delhi – 110016, India ' University School of Information and Communication Technology, Guru Gobind Singh Indraprastha University, Dwarka, Delhi 110078, India
Abstract: Fingerprint-based individual authentication has been the most trusted and tested biometric among the biometrics traits. In the past two decades, many methods have been developed for fingerprint matching, but still there is a huge scope of improvement. This paper presents the rotation invariant fingerprint matching method, which is based on local directional pattern (LDP) features computed directly from grey values of a fingerprint image. For matching the extracted LDP histogram features, we have used single hidden layer feedforward neural networks (SLFNN). Six training algorithms namely, resilient propagation (RP), scaled conjugate gradient (SCG), gradient decent with all four variants [GD, GDM, GDA, GDX (refer to Table 2 for details)] are used for evaluating the matching performance and convergence time. The results show that the proposed features are invariant to the rotation and also suitable for fingerprint matching using SLFNN. The results also demonstrate that RP is better in performance than other investigated algorithms.
Keywords: image-based fingerprint verification; fingerprint matching; local directional patterns; LDP histogram; SLFNN; rotation invariant; fingerprints; neural networks; biometrics; individual authentication.
International Journal of Computational Vision and Robotics, 2014 Vol.4 No.4, pp.330 - 348
Available online: 29 Oct 2014 *Full-text access for editors Access for subscribers Purchase this article Comment on this article