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    Deep learning for biometric face recognition: experimental study on benchmark data sets

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    Authors
    Selitskaya, Natalya
    Sielicki, S.
    Jakaite, Livija
    Schetinin, Vitaly
    Evans, F.
    Conrad, Marc
    Sant, Paul
    Issue Date
    2020-06-09
    Subjects
    biometrics
    machine learning
    Subject Categories::G760 Machine Learning
    
    Metadata
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    Other Titles
    Deep Biometrics
    Abstract
    There are still problems in applications of Machine Learning for face recognition. Such factors as lighting conditions, head rotations, emotions, and view angles affect the recognition accuracy. A large number of recognition subjects requires complex class boundaries. Deep Neural Networks have provided efficient solutions, although their implementations require massive computations for evaluation and minimisation of error functions. Gradient algorithms provide iterative minimisation of the error function. A maximal performance is achieved if parameters of gradient algorithms and neural network structures are properly set. The use of pairwise neural network structures often improves the performance because such structures require a small set of optimisation parameters. The experiments have been conducted on some face biometric benchmark data sets, and the main findings are presented in the form of a tutorial.
    Citation
    Selitskaya N, Sielicki S, Jakaite L, Schetinin V, Evans F, Conrad M, Sant P (2020) 'Deep learning for biometric face recognition: experimental study on benchmark data sets', in Jiang R, Li C, Crookes D, Meng W, Rosenberger C (ed(s).). Deep Biometrics, 1 edn, London: Springer International Publishing pp.71-97.
    Publisher
    Springer International Publishing
    URI
    http://hdl.handle.net/10547/624023
    DOI
    10.1007/978-3-030-32583-1_5
    Additional Links
    https://link.springer.com/chapter/10.1007%2F978-3-030-32583-1_5
    Type
    Book chapter
    Language
    en
    ISBN
    9783030325824
    ae974a485f413a2113503eed53cd6c53
    10.1007/978-3-030-32583-1_5
    Scopus Count
    Collections
    Computing

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