Abstract
In recent years, touchless fingerprint recognition systems have become a reliable alternative to the conventional touch fingerprint recognition system. Furthermore, with the current health crisis caused by the emergence of SARS-COV 2, the implementation of technologies that allow us to avoid direct contact with readers and devices arises as an urgent need. This article shows a system for fingerprint segmentation, filtering, and enhancement by fingerphoto technology. The dataset was acquired from smartphones on uncontrolled conditions. The proposed fingerprint recognition scheme provides an efficient preview of an automated identification system that can be extended to numerous security or administration applications. Skin model segmentation presents an accuracy of 95% over other solutions for background removal in the state of the art. For fingerphoto extraction, results were evaluated with the NIST Finger Image Quality \(NFIQ\) of the National Institute of Standards and Technology \(NIST\).
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López-Jiménez, M.E., Virgilio-González, V.R., Aguilar-Figueroa, R., Virgilio-González, C.D. (2021). Touchless Fingerphoto Extraction Based on Deep Learning and Image Processing Algorithms; A Preview. In: Batyrshin, I., Gelbukh, A., Sidorov, G. (eds) Advances in Computational Intelligence. MICAI 2021. Lecture Notes in Computer Science(), vol 13067. Springer, Cham. https://doi.org/10.1007/978-3-030-89817-5_19
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