Abstract
Although face detection has taken a big step forward with the development of anchor based face detector, the issue of effective detection of faces with different scales still remains. To solve this problem, we present an one-stage face detector, named Single Shot Attention-Based Face Detector (AFD), which enables accurate detection of multi-scale faces with high efficiency, especially for small faces. Specifically, AFD consists of two inter-connected modules, namely attention proposal module (APM) and face detection module (FDM). The former aims to generate the attention region and coarsely refine the anchors. The latter takes the output from APM as input and further improve the detection results. We obtain state-of-the-art results on common face detection benchmarks, i.e. FDDB and WIDER FACE, and can run at 20 FPS on a Nvidia Titan X (Pascal) for VGA-resolution images.
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Acknowledgments
This work was supported by the Chinese National Natural Science Foundation Projects #61473291, #61572536, #61572501, #61573356, the National Key Research and Development Plan (Grant No. 2016YFC0801002), and AuthenMetric R&D Funds.
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Zhuang, C., Zhang, S., Zhu, X., Lei, Z., Li, S.Z. (2018). Single Shot Attention-Based Face Detector. In: Zhou, J., et al. Biometric Recognition. CCBR 2018. Lecture Notes in Computer Science(), vol 10996. Springer, Cham. https://doi.org/10.1007/978-3-319-97909-0_31
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