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Single-Shot Scale-Aware Network for Real-Time Face Detection

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Abstract

In this work, we describe a single-shot scale-aware convolutional neural network based face detector (SFDet). In comparison with the state-of-the-art anchor-based face detection methods, the main advantages of our method are summarized in four aspects. (1) We propose a scale-aware detection network using a wide scale range of layers associated with appropriate scales of anchors to handle faces with various scales, and describe a new equal density principle to ensure anchors with different scales to be evenly distributed on the image. (2) To improve the recall rates of faces with certain scales (e.g., the scales of the faces are quite different from the scales of designed anchors), we design a new anchor matching strategy with scale compensation. (3) We introduce an IoU-aware weighting scheme for each training sample in classification loss calculation to encode samples accurately in training process. (4) Considering the class imbalance issue, a max-out background strategy is used to reduce false positives. Several experiments are conducted on public challenging face detection datasets, i.e., WIDER FACE, AFW, PASCAL Face, FDDB, and MAFA, to demonstrate that the proposed method achieves the state-of-the-art results and runs at 82.1 FPS for the VGA-resolution images.

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Notes

  1. We denote the reference bounding box as “anchor box”, which is also called “anchor” for simplicity, as in Ren et al. (2017). However, in Liu et al. (2016), it is also called “default box”.

  2. We denote the very tiny faces or some faces with the certain scales located around the middle of two scales of designed anchors as the outlier faces.

  3. The Jaccard overlap (Erhan et al. 2014) is also known as intersection-over-union, which is used to measure the overlap rate between two regions here.

  4. Since the ratio between positive and negative anchors is set to 1:3, we use \(\lambda =4\) to balance the classification and regression losses in training.

  5. Empirically, we set \(C=3\) in our experiments.

  6. The negative anchor indicates that the anchor is not matched to any ground truth bounding box.

  7. http://mmlab.ie.cuhk.edu.hk/projects/WIDERFace/index.html.

  8. https://www.cs.cmu.edu/~peiyunh/tiny/.

  9. http://web.engr.illinois.edu/~dhoiem/projects/detectionAnalysis.

  10. http://www.ics.uci.edu/~xzhu/face/.

  11. http://host.robots.ox.ac.uk/pascal/VOC/voc2012/.

  12. http://vis-www.cs.umass.edu/fddb/index.html.

  13. http://www.escience.cn/people/geshiming/mafa.html.

  14. https://github.com/BVLC/caffe/pull/2213.

  15. For example, in our FPN architecture, there are mainly five steps, i.e., (1) input low-level features L (\(15\times 15\)), (2) use operation with stride 2 to get high-level features H (\(8\times 8\)), (3) upsample H to H-up (\(16\times 16\)), (4) crop H-up to H-up-crop (\(15\times 15\)), and (5) add L and H-up-Crop.

  16. https://bitbucket.org/deeplab/deeplab-public.

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Acknowledgements

This work was supported by the Chinese National Natural Science Foundation Projects #61876178, #61473291, #61806196, the National Key Research and Development Plan (Grant No. 2016YFC0801002), JD Grapevine Plan and AuthenMetric R&D Funds.

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Authors and Affiliations

  1. CBSR & NLPR, Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Shifeng Zhang, Zhen Lei & Stan Z. Li

  2. University of Chinese Academy of Sciences, Beijing, China

    Shifeng Zhang, Zhen Lei & Stan Z. Li

  3. JD Digits, Mountain View, CA, USA

    Longyin Wen

  4. JD AI Research, Beijing, China

    Hailin Shi

  5. Computer Science Department, University at Albany, SUNY, Albany, NY, USA

    Siwei Lyu

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Zhang, S., Wen, L., Shi, H. et al. Single-Shot Scale-Aware Network for Real-Time Face Detection. Int J Comput Vis 127, 537–559 (2019). https://doi.org/10.1007/s11263-019-01159-3

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