In the era of artificial intelligence, privacy has become a paramount concern, especially within intelligent transportation systems (ITS) where pedestrians are frequently captured by vehicle-mounted cameras for deep learning model training. To address this, we introduce 3PFS, a novel method designed to protect pedestrian privacy via face swapping while preserving the utility of processed images. Our method consists of a pedestrian detector, a face detector, a pre-processing module, a source face selection algorithm, and a face swapping algorithm. After detecting pedestrians and their corresponding faces, the pre-processing module enhances image quality. Our unique source face selection algorithm then chooses an appropriate face from our source face library, which is subsequently swapped with the target face using a face swapping algorithm. Notably, with the combination of a pedestrian tracking algorithm, our 3PFS is well-suited for video anonymization. Additionally, we propose a comprehensive evaluation strategy to evaluate the performance of pedestrian anonymization methods. We validate the effectiveness of 3PFS through extensive experiments on a dataset we created based on the publicly available JAAD dataset and on videos captured using our robotic wheelchair.


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    Titel :

    3PFS: Protecting Pedestrian Privacy Through Face Swapping


    Beteiligte:
    Zhao, Zixian (Autor:in) / Zhang, Xingchen (Autor:in) / Demiris, Yiannis (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    01.11.2024


    Format / Umfang :

    3563546 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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