Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
}
TY - GEN
T1 - Deep neural network based multi-resolution face detection for smart cities
AU - Storey, Gary
AU - Jiang, Richard
AU - Bouridane, Ahmed
AU - Dinakaran, Ranjith
AU - Li, Chang-Tsun
N1 - Author was employed at another UK HEI at the time of submission and was deposited at Northumbria University Repository, see link http://nrl.northumbria.ac.uk/39855/
PY - 2018/6/27
Y1 - 2018/6/27
N2 - 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).
AB - 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).
M3 - Conference contribution/Paper
BT - International Conference on Information Society and Smart Cities 2018
ER -