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    • UNIR REVISTAS
    • Revista IJIMAI
    • 2020
    • vol. 6, nº 1, march 2020
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    • UNIR REVISTAS
    • Revista IJIMAI
    • 2020
    • vol. 6, nº 1, march 2020
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    A Convolution Neural Network Engine for Sclera Recognition

    Autor: 
    Harish, B S
    ;
    Maheshan, M S
    ;
    Nagadarshan, N
    Fecha: 
    03/2020
    Palabra clave: 
    biometry; deep learning; convolutional neural network (CNN); sclera recognition; IJIMAI
    Tipo de Ítem: 
    article
    URI: 
    https://reunir.unir.net/handle/123456789/12692
    DOI: 
    https://doi.org/10.9781/ijimai.2019.03.006
    Dirección web: 
    https://www.ijimai.org/journal/bibcite/reference/2715
    Open Access
    Resumen:
    The world is shifting to the digital era in an enormous pace. This rise in the digital technology has created plenty of applications in the digital space, which demands a secured environment for transacting and authenticating the genuineness of end users. Biometric systems and its applications has seen great potentials in its usability in the tech industries. Among various biometric traits, sclera trait is attracting researchers from experimenting and exploring its characteristics for recognition systems. This paper, which is first of its kind, explores the power of Convolution Neural Network (CNN) for sclera recognition by developing a neural model that trains its neural engine for a recognition system. To do so, the proposed work uses the standard benchmark dataset called Sclera Segmentation and Recognition Benchmarking Competition (SSRBC 2015) dataset, which comprises of 734 images which are captured at different viewing angles from 30 different classes. The proposed methodology results showcases the potential of neural learning towards sclera recognition system.
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