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To Wrap, or Not to Wrap: Examining the Distinctions Between Model Implementations of Face Recognition on Mobile Devices in an Automatic Attendance System

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Abstract

As a continuation of the work that we had done in the past to develop an automatic attendance system for the campus of our institution, the following is stated—In this study, we will examine the differences in the implementation approaches of a face recognition model on actual mobile devices (iOS and Android), as well as its performance. Specifically, we will look at the discrepancies between these two categories. In particular, we will investigate the ways in which these distinctions influence the precision of face recognition predictions as well as the amount of work that is required of devices in order for them to use a machine learning model, examine the advantages and disadvantages of the model encoding approach that is shared by the TensorFlow and CoreML frameworks, as well as how it helps to the overall success of the AttendanceKit system.

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Authors and Affiliations

  1. International University, Ho Chi Minh City, Vietnam

    Trung-Dung Tran & Tu-Nga Ly

  2. Vietnam National University, Ho Chi Minh City, Vietnam

    Tu-Nga Ly

Authors
  1. Trung-Dung Tran
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  2. Tu-Nga Ly
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Correspondence to Tu-Nga Ly.

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This article is part of the topical collection “Future Data and Security Engineering 2022” guest edited by Tran Khanh Dang.

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Tran, TD., Ly, TN. To Wrap, or Not to Wrap: Examining the Distinctions Between Model Implementations of Face Recognition on Mobile Devices in an Automatic Attendance System. SN COMPUT. SCI. 4, 729 (2023). https://doi.org/10.1007/s42979-023-02185-2

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  • DOI: https://doi.org/10.1007/s42979-023-02185-2

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