Dang, L.V., Makhanov, S.S., Hieu, L.C. ORCID: 0000-0002-5168-2297, Packianather, M. S., Minh, H.L. and Quoc, L.H. (2019) Development of a fingerprint singularity detection method based on moment invariants for biometrics and medical applications. In: 7th International Conference on the Development of Biomedical Engineering in Vietnam (BME7): Translational Health Science and Technology for Developing Countries. IFMBE Proceedings, 69 (3). Springer Verlag, Singapore, pp. 567-573. ISBN 978-9811358586 ISSN 1680-0737 (Online) (doi:https://doi.org/10.1007/978-981-13-5859-3_85)

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Abstract

The biometric technologies have long been used for identification and authentication purposes; and fingerprint is one of the most widely used biometric technologies. In medicine and healthcare applications, biometric systems are used to identify patients and retrieve crucial medical records. In this paper, we propose a fingerprint singularity detection algorithm based on a very well-known pattern recognition technique. The successfully developed algorithm was tested for different fingerprint image resolutions, noise levels, and it was directly compared to the traditional technique, Poincare index which is the scalar values representing the geometrical behavior of basic patterns. The test of the proposed algorithm shows the outperformed results in both the high noise and low resolution images. Especially, the descriptors can be extracted directly from the suspect original and sample fingerprint images. The proposed method is therefore robust and can be adopted to any special descriptors rather than the pure core and delta points. With the recent advancement in data science, the successfully developed algorithm is potential for development of innovative biometric and medical applications, especially for telehealth and e-health systems.

Item Type:	Conference Proceedings
Title of Proceedings:	7th International Conference on the Development of Biomedical Engineering in Vietnam (BME7): Translational Health Science and Technology for Developing Countries
Uncontrolled Keywords:	biometrics, poincare index, singularity detection, medical records, patient identification
Subjects:	T Technology > TA Engineering (General). Civil engineering (General)
Faculty / School / Research Centre / Research Group:	Faculty of Engineering & Science Faculty of Engineering & Science > School of Engineering (ENG)
Related URLs:	Publisher
Last Modified:	31 Jul 2019 11:11
URI:	http://gala.gre.ac.uk/id/eprint/22480

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