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Enhanced Security Framework for Enabling Facial Recognition in Autonomous Shuttles Public Transportation During COVID-19

Abstract : Autonomous Vehicles (AVs) can potentially reduce the accident risk while a human is driving. They can also improve the public transportation by connecting city centers with main mass transit systems. The development of technologies that can provide a sense of security to the passenger when the driver is missing remains a challenging task. Moreover, such technologies are forced to adopt to the new reality formed by the COVID-19 pandemic, as it has created significant restrictions to passenger mobility through public transportation. In this work, an image-based approach, supported by novel AI algorithms, is proposed as a service to increase autonomy of non-fully autonomous people such as kids, grandparents and disabled people. The proposed real-time service, can identify family members via facial characteristics and efficiently ignore face masks, while providing notifications for their condition to their supervisor relatives. The envisioned AI-supported security framework, apart from enhancing the trust to autonomous mobility, could be advantageous in other applications also related to domestic security and defense.
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https://hal.inria.fr/hal-03287659
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Submitted on : Thursday, July 15, 2021 - 6:09:44 PM
Last modification on : Thursday, July 15, 2021 - 6:34:46 PM
Long-term archiving on: : Saturday, October 16, 2021 - 7:02:41 PM

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Dimitris Tsiktsiris, Antonios Lalas, Minas Dasygenis, Konstantinos Votis, Dimitrios Tzovaras. Enhanced Security Framework for Enabling Facial Recognition in Autonomous Shuttles Public Transportation During COVID-19. 17th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Jun 2021, Hersonissos, Crete, Greece. pp.145-154, ⟨10.1007/978-3-030-79150-6_12⟩. ⟨hal-03287659⟩

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