Deep neural network based multi-resolution face detection for smart cities

Storey, Gary, Jiang, Richard, Bouridane, Ahmed, Dinakaran, Ranjith and Li, Chang-Tsun (2018) Deep neural network based multi-resolution face detection for smart cities. In: ISC 2018 - International Conference on Information Society and Smart Cities, 27th - 28th June 2018, Cambridge, UK.

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Storey et al - Deep neural network based multi-resolution face detection for smart cities AAM.pdf - Accepted Version

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Abstract

Face detection from unconstrained “in the wild” images such as those obtained from CCTV and other image capture devices used within urban environments can provide a rich source of information about citizens within the urban environments benefiting tasks such as pedestrians counting and biometric security. In recent years Deep Convolutional Neural Networks have revolutionized the state-of-the-art for face detection tasks, for utilization within smart cities through leveraging existing CCTV networks, some challenges still exist such as the scale and resolution of the faces within an image. We present a single multi-resolution deep neural network and trained on publicly available image databases that splits the face detection task into small and large face detection at a feature level. We show how our proposed network outperforms single task face detection Faster R-CNN architectures across three challenging test sets (AFW, AFLW and Wider Face).

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Face Detection, Urban Computing, Biometric-as-a-service, Deep Neural Networks
Subjects: G400 Computer Science
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Paul Burns
Date Deposited: 03 Jul 2019 15:44
Last Modified: 11 Oct 2019 09:36
URI: http://nrl.northumbria.ac.uk/id/eprint/39855

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