Abstract
Face detection in unconstrained environment is a challenge problem. Recent studies show that deep convolutional networks (DCNs) have achieved outstanding performance on this task, but most of them have multiple stages (e.g., region proposal, classification), which are complex and time-consuming in practice. In this paper, we propose a fully convolutional network (FCN) framework which can be trained straightforward in an end-to-end manner. In our network, hierarchical feature layers with different resolutions are used to detect different scale faces. For each hierarchical layer, a specific default boxes set with different aspect ratios and scales is associated with each map cell. At prediction time, the network generates confidence scores for the default boxes and produces offsets of default boxes to get better bounding boxes of faces. The predictions of each hierarchical layer are combined into final detection result. Experimental results on the AFW and FDDB datasets confirm the effectiveness of our method.
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Acknowledgments
This work was partially supported by the National Natural Science Foundations of China (Grant nos. 61472386 and 61502444) and the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant XDA 06040103).
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Lv, JJ., Feng, YJ., Zhou, XD., Zhou, X. (2016). Face Detection Using Hierarchical Fully Convolutional Networks. In: Tan, T., Li, X., Chen, X., Zhou, J., Yang, J., Cheng, H. (eds) Pattern Recognition. CCPR 2016. Communications in Computer and Information Science, vol 662. Springer, Singapore. https://doi.org/10.1007/978-981-10-3002-4_23
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DOI: https://doi.org/10.1007/978-981-10-3002-4_23
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