Yu, Z;
Yin, J;
Zhang, Q;
Yang, W;
Xue, J-H;
Liao, Q;
(2021)
Hourglass Face Detector for Hard Face.
In:
2021 International Joint Conference on Neural Networks (IJCNN).
IEEE
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Abstract
Face detection is an upstream task of facial image analysis. In many real-world scenarios, we need to detect small, occluded or dense faces that are hard to detect, but hard face detection is a challenging task in particular considering the balance between accuracy and inference speed for real-world applications. This paper proposes an Hourglass Face Detector (HFD) for hard face by developing a deep one-stage fully-convolutional hourglass network, which achieves an excellent balance between accuracy and inference speed. To this end, the HFD firstly shrinks a feature map by a series of stridden convolutional layers rather than pooling layers, so that useful subtle information is preserved better. Secondly, it exploits context information by merging fine-grained shallow feature maps with deep ones full of semantic information, making a better fusion of detailed information and semantic information to achieve a better detection of small faces. Moreover, the HFD exploits prior and multiscale information from the training data to enhance its scale-invariance and adaptability of anchor scales. Compared with the SSH and S3FD methods, the HFD can achieve a better performance in average precision on detecting hard faces as well as a quicker inference. Experiments on the WIDER FACE and FDDB datasets demonstrate the superior performance of our proposed method.
Type: | Proceedings paper |
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Title: | Hourglass Face Detector for Hard Face |
Event: | 2021 International Joint Conference on Neural Networks (IJCNN) |
Dates: | 18 July 2021 - 22 July 2021 |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.1109/ijcnn52387.2021.9533674 |
Publisher version: | https://doi.org/10.1109/IJCNN52387.2021.9533674 |
Language: | English |
Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions. |
UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science |
URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10135635 |
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