Deep Learning-Based Automated Detection of Inappropriate Face Image Attributes for ID Documents - 12th IFIP WG 5.5/SOCOLNET Advanced Doctoral Conference on Computing, Electrical and Industrial Systems, DoCEIS 2021 Access content directly
Conference Papers Year : 2021

Deep Learning-Based Automated Detection of Inappropriate Face Image Attributes for ID Documents

Abstract

A face photo forms a fundamental element of almost every identity document such as national ID cards, passports, etc. The governmental agencies issuing such documents may set slightly different requirements for a face image to be acceptable. Nevertheless, some are too critical to avoid, such as mouth closedness, eyes openness and no veil-over-face. In this paper, we aim to address the problem of fully automating the inspection of these 3 characteristics, thereby enabling the face capturing devices to determine, as soon as a face image is taken, if any of them is invalid or not. To accomplish this, we propose a deep learning-based approach by defining model architectures that are lightweight enough to enable real-time inference on resource-constrained devices with a particular focus on prediction accuracy. Lastly, we showcase the performance and efficiency of our approach, which is found to surpass two well-known off-the-shelf solutions in terms of overall precision.
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hal-03685928 , version 1 (02-06-2022)

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Amineh Mazandarani, Pedro Amaral, Paulo Da Pinto, S. Hosseini. Deep Learning-Based Automated Detection of Inappropriate Face Image Attributes for ID Documents. 12th Doctoral Conference on Computing, Electrical and Industrial Systems (DoCEIS), Jul 2021, Costa de Caparica, Portugal. pp.243-253, ⟨10.1007/978-3-030-78288-7_23⟩. ⟨hal-03685928⟩
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