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      Towards a high-precision contactless fingerprint scanner for biometric authentication

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          Abstract

          The raging COVID-19 pandemic accentuates the urgent and compelling need for non-contact fingerprinting biometric authentication devices to mitigate the transmission of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and other contagious infections. Current approaches to contactless fingerprinting scanners suffer limitations ranging from poor compatibility with two-dimensional equivalent touch-based fingerprint images to perspective distortions, inconstant resolution, motion blur images and low correlation factors. Herein, these constraints are tackled by implementing a system that enables the positioning of the target finger(s) at fixed vertical and horizontal distances away from the camera lens without the physical contact of the fingers with the device framework during scanning. A high-precision fingerprint pattern recognition of up to 97.51 % correlation factor has been achieved, using this contactless method, by varying the background illuminating light and implementing two-dimensional imaging techniques and near-constant resolution. Additionally, a convenient contactless fingerprint acquisition process is reinforced through a unique architectural design.

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          Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and coronavirus disease-2019 (COVID-19): The epidemic and the challenges

          Chih-Cheng Lai, Tzu-Ping Shih, Wen-Chien Ko … (2020)
          Highlights • Emergence of 2019 novel coronavirus (2019-nCoV) in China has caused a large global outbreak and major public health issue. • At 9 February 2020, data from the WHO has shown >37 000 confirmed cases in 28 countries (>99% of cases detected in China). • 2019-nCoV is spread by human-to-human transmission via droplets or direct contact. • Infection estimated to have an incubation period of 2–14 days and a basic reproduction number of 2.24–3.58. • Controlling infection to prevent spread of the 2019-nCoV is the primary intervention being used.
          Article 0 comments Cited 2477 times – based on 0 reviews     Review now
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            Filterbank-based fingerprint matching.

            A.K. Jain, S. Prabhakar, L. Hong … (2000)
            With identity fraud in our society reaching unprecedented proportions and with an increasing emphasis on the emerging automatic personal identification applications, biometrics-based verification, especially fingerprint-based identification, is receiving a lot of attention. There are two major shortcomings of the traditional approaches to fingerprint representation. For a considerable fraction of population, the representations based on explicit detection of complete ridge structures in the fingerprint are difficult to extract automatically. The widely used minutiae-based representation does not utilize a significant component of the rich discriminatory information available in the fingerprints. Local ridge structures cannot be completely characterized by minutiae. Further, minutiae-based matching has difficulty in quickly matching two fingerprint images containing a different number of unregistered minutiae points. The proposed filter-based algorithm uses a bank of Gabor filters to capture both local and global details in a fingerprint as a compact fixed length FingerCode. The fingerprint matching is based on the Euclidean distance between the two corresponding FingerCodes and hence is extremely fast. We are able to achieve a verification accuracy which is only marginally inferior to the best results of minutiae-based algorithms published in the open literature. Our system performs better than a state-of-the-art minutiae-based system when the performance requirement of the application system does not demand a very low false acceptance rate. Finally, we show that the matching performance can be improved by combining the decisions of the matchers based on complementary (minutiae-based and filter-based) fingerprint information.
            Article 0 comments Cited 84 times – based on 0 reviews     Review now
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              Complex wavelet structural similarity: a new image similarity index.

              Mehul P Sampat, Zhou Wang, Shalini Gupta … (2009)
              We introduce a new measure of image similarity called the complex wavelet structural similarity (CW-SSIM) index and show its applicability as a general purpose image similarity index. The key idea behind CW-SSIM is that certain image distortions lead to consistent phase changes in the local wavelet coefficients, and that a consistent phase shift of the coefficients does not change the structural content of the image. By conducting four case studies, we have demonstrated the superiority of the CW-SSIM index against other indices (e.g., Dice, Hausdorff distance) commonly used for assessing the similarity of a given pair of images. In addition, we show that the CW-SSIM index has a number of advantages. It is robust to small rotations and translations. It provides useful comparisons even without a preprocessing image registration step, which is essential for other indices. Moreover, it is computationally less expensive.
              Article 0 comments Cited 68 times – based on 0 reviews     Review now
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                Author and article information

                Journal
                Title: Array
                Publisher: Published by Elsevier Inc.
                ISSN (Print): 2590-0056
                ISSN (Electronic): 2590-0056
                Publication date PMC-release: 6 August 2021
                Publication date (Electronic): 6 August 2021
                Electronic Location Identifier: 100083
                Affiliations
                [a ]Department of Physics, University of Lagos, Akoka-Yaba, 100213, Lagos, Nigeria
                [b ]Department of Electrical and Electronics Engineering, University of Lagos, Akoka-Yaba, 100213, Lagos, Nigeria
                [c ]Department of Chemistry, University of Lagos, Akoka-Yaba, 100213, Lagos, Nigeria
                Author notes
                [∗ ]Corresponding author.
                Article
                Publisher Item ID: S2590-0056(21)00031-X Publisher ID: 100083
                DOI: 10.1016/j.array.2021.100083
                PMC ID: 8343378
                SO-VID: 7bdc5054-6e3e-443c-a6c3-2a90f8768b5f
                Copyright © © 2021 Published by Elsevier Inc.
                License:

                Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.

                History
                Date received : 11 February 2021
                Date revision received : 22 June 2021
                Date accepted : 28 July 2021
                Categories
                Subject: Article

                Keywords: authentication,biometric,blue light,covid-19,fingerprint,pattern recognition
                Data availability:
                Keywords: authentication, biometric, blue light, covid-19, fingerprint, pattern recognition

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