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    • Revista IJIMAI
    • 2022
    • vol. 7, nº 6, september 2022
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    • UNIR REVISTAS
    • Revista IJIMAI
    • 2022
    • vol. 7, nº 6, september 2022
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    Edge Face Recognition System Based on One-Shot Augmented Learning

    Autor: 
    Jiménez-Bravo, Diego M.
    ;
    Lozano Murciego, Álvaro
    ;
    Sales, A.
    ;
    Augusto Silva, Luis
    ;
    De La Iglesia, Daniel H.
    Fecha: 
    09/2022
    Palabra clave: 
    data augmentation; edge computing; face detection; one-shot learning; security; IJIMAI
    Revista / editorial: 
    International Journal of Interactive Multimedia and Artificial Intelligence (IJIMAI)
    Tipo de Ítem: 
    article
    URI: 
    https://reunir.unir.net/handle/123456789/13899
    DOI: 
    https://doi.org/10.9781/ijimai.2022.09.001
    Dirección web: 
    https://ijimai.org/journal/bibcite/reference/3168
    Open Access
    Resumen:
    There is growing concern among users of computer systems about how their data is handled. In this sense, IT (Information Technology) professionals are not unaware of this problem and are looking for solutions to meet the requirements and concerns of their users. During the last few years, various techniques and technologies have emerged that allow us to answer to the problem posed by users. Technologies such as edge computing and techniques such as one-shot learning and data augmentation enable progress in this regard. Thus, in this article, we propose the creation of a system that makes use of these techniques and technologies to solve the problem of face recognition and form a low-cost security system. The results obtained show that the combination of these techniques is effective in most of the face detection algorithms and allows an effective solution to the problem raised.
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