Deakin University

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A privacy-aware architecture at the edge for autonomous real-time identity reidentification in crowds

journal contribution
posted on 2023-02-22, 04:15 authored by S A Miraftabzadeh, P Rad, K K R Choo, M Jamshidi
The capability to perform identity reidentification in a crowd (e.g., from video feeds from a network of cameras, and social media platforms, such as Facebook and Instagram) efficiently and effectively is increasingly important, as evident in recent real-world events (e.g., terrorist attacks on places of mass gatherings in different countries). However, real-time reidentification in a network of cameras, such as those deployed in a smart city, and from other sources, such as social media platforms, remains a challenging task. In this paper, a new embedding algorithm pipeline is presented to extract and administrate the crowd-sourced facial image features (e.g., social media platforms and multicameras in a dense crowd, such as a stadium or airport). The proposed facial embedding is a privacy-aware parameterized function, which maps facial images to high-dimensional vectors in order to facilitate the identification and tracking of individuals. In other words, we are able to uniquely identify person(s) of interest, without the need to determine their true identity. To extract the facial embedding information in crowds, concurrent residual neural network (ResNet) embedding pipeline for each camera is proposed. Specifically, facial embedding feature vectors are generated in real-time by each camera using the proposed enhanced ResNet architecture, which is trained with vectorized-l2-loss function for face recognition. The multivariate kernel density estimation matching algorithm is then applied to facial embedding pipelines generated by cameras at the fog cloud for identity reidentification and security verification. This allows us to ensure the privacy of individuals captured by the camera without compromising on the capability for identity reidentification. Evaluations using mixed datasets in real-time demonstrate that our proposed approach achieves a 2.6% accuracy over other state-of-the-art approaches.



IEEE Internet of Things Journal




2936 - 2946



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