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Fingerprint Matching Using Rotational Invariant Orientation Local Binary Pattern Descriptor and Machine Learning Techniques

Fingerprint Matching Using Rotational Invariant Orientation Local Binary Pattern Descriptor and Machine Learning Techniques

Ravinder Kumar
Copyright: © 2020 |Pages: 19
ISBN13: 9781799824602|ISBN10: 1799824608|EISBN13: 9781799824619
DOI: 10.4018/978-1-7998-2460-2.ch048
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MLA

Kumar, Ravinder. "Fingerprint Matching Using Rotational Invariant Orientation Local Binary Pattern Descriptor and Machine Learning Techniques." Cognitive Analytics: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, IGI Global, 2020, pp. 943-961. https://doi.org/10.4018/978-1-7998-2460-2.ch048

APA

Kumar, R. (2020). Fingerprint Matching Using Rotational Invariant Orientation Local Binary Pattern Descriptor and Machine Learning Techniques. In I. Management Association (Ed.), Cognitive Analytics: Concepts, Methodologies, Tools, and Applications (pp. 943-961). IGI Global. https://doi.org/10.4018/978-1-7998-2460-2.ch048

Chicago

Kumar, Ravinder. "Fingerprint Matching Using Rotational Invariant Orientation Local Binary Pattern Descriptor and Machine Learning Techniques." In Cognitive Analytics: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, 943-961. Hershey, PA: IGI Global, 2020. https://doi.org/10.4018/978-1-7998-2460-2.ch048

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Abstract

The objective of this article is to propose rotation invariant fingerprint descriptor, and a faster and better generalized performance classifier. The author proposes a new multi-resolution analysis based fingerprint descriptor, computed from fingerprint orientation pattern called as orientation local binary pattern (OLBP). The feature vector is constructed by concatenating the OLBP histograms obtained from tessellated ROI of distorted fingerprint images. Secondly, the author proposes a hybrid classifier, which combines a powerful extreme learning machine (ELM) and a well generalized resilient propagation (RPROP). Finally, they propose two hybrid training algorithms using ELM and RPROP. The matching accuracy of 99.9% validates the performance of the proposed OLBP features and the proposed hybrid classification algorithms perform better as compared to the original ELM.

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