Skip to main content
SpringerLink
Log in

EfficientSRFace: An Efficient Network with Super-Resolution Enhancement for Accurate Face Detection

  • Conference paper
  • First Online:
Pattern Recognition (ACPR 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14407))

Included in the following conference series:

  • 300 Accesses

Abstract

In face detection, low-resolution faces, such as numerous small faces of a human group in a crowded scene, are common in dense face prediction tasks. They usually contain limited visual clues and make small faces less distinguishable from the other small objects, which poses great challenge to accurate face detection. Although deep convolutional neural network has significantly promoted the research on face detection recently, current deep face detectors rarely take into account low-resolution faces and are still vulnerable to the real-world scenarios where massive amount of low-resolution faces exist. Consequently, they usually achieve degraded performance for low-resolution face detection. In order to alleviate this problem, we develop an efficient detector termed EfficientSRFace by introducing a feature-level super-resolution reconstruction network for enhancing the feature representation capability of the model. This module plays an auxiliary role in the training process, and can be removed during the inference without increasing the inference time. Extensive experiments on public benchmarking datasets, such as FDDB and WIDER Face, show that the embedded image super-resolution module can significantly improve the detection accuracy at the cost of a small amount of additional parameters and computational overhead, while helping our model achieve competitive performance compared with the state-of-the-arts.

Supported by the Natural Science Foundation of China (NSFC) under grants 62173186 and 62076134.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 59.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 79.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Vesdapunt, N., Wang, B.: Crface: confidence ranker for model-agnostic face detection refinement. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1674–1684 (2021)

    Google Scholar 

  2. Tang, X., Du, D.K., He, Z., Liu, J.: Pyramidbox: a context-assisted single shot face detector. In: Proceedings of the European Conference on Computer Vision, pp. 797–813 (2018)

    Google Scholar 

  3. Ming, X., Wei, F., Zhang, T., Chen, D., Wen, F.: Group sampling for scale invariant face detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3446–3456 (2019)

    Google Scholar 

  4. Liu, Y., Wang, F., Deng, J., Zhou, Z., Sun, B., Li, H.: Mogface: towards a deeper appreciation on face detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4093–4102 (2022)

    Google Scholar 

  5. Zhang, F., Fan, X., Ai, G., Song, J., Qin, Y., Wu, J.: Accurate face detection for high performance. arXiv preprint arXiv:1905.01585, pp. 1–9 (2019)

  6. Chi, C., Zhang, S., Xing, J., Lei, Z., Li, S.Z., Zou, X.: Selective refinement network for high performance face detection. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 8231–8238 (2019)

    Google Scholar 

  7. Li, J., et al.: DSFD: dual shot face detector. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5060–5069 (2019)

    Google Scholar 

  8. Qi, D., Tan, W., Yao, Q., Liu, J.: Yolo5face: why reinventing a face detector. In: Proceedings of the European Conference on Computer Vision Workshops, pp. 228–244 (2022)

    Google Scholar 

  9. Yoo, Y., Han, D., Yun, S.: EXTD: extremely tiny face detector via iterative filter reuse. arXiv preprint arXiv:1906.06579, pp. 1–11 (2019)

  10. He, Y., Xu, D., Wu, L., Jian, M., Xiang, S., Pan, C.: LFFD: a light and fast face detector for edge devices. arXiv preprint arXiv:1904.10633, pp. 1–10 (2019)

  11. Wang, G., Li, J., Wu, Z., Xu, J., Shen, J., Yang, W.: EfficientFace: An Efficient Deep Network with Feature Enhancement for Accurate Face Detection. Multimedia Systems, pp. 1–15 (2023)

    Google Scholar 

  12. Liu, W., et al.: SSD: single shot multibox detector. In: Proceedings of the European Conference on Computer Vision, pp. 21–37 (2016)

    Google Scholar 

  13. Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016)

    Google Scholar 

  14. Duan, K., Bai, S., Xie, L., Qi, H., Huang, Q., Tian, Q.: Centernet: keypoint triplets for object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 6569–6578 (2019)

    Google Scholar 

  15. Tan, M., Pang, R., Le, Q.V.: Efficientdet: scalable and efficient object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10781–10790 (2020)

    Google Scholar 

  16. Zhang, C., Xu, X., Tu, D.: Face detection using improved faster RCNN. arXiv preprint arXiv:1802.02142, pp. 1–9 (2018)

  17. Zhang, S., et al.: Improved selective refinement network for face detection. arXiv preprint arXiv:1901.06651, pp. 1–8 (2019)

  18. Zhang, Y., Xu, X., Liu, X.: Robust and high performance face detector. arXiv preprint arXiv:1901.02350, pp. 1–9 (2019)

  19. Zhu, Y., Cai, H., Zhang, S., Wang, C., Xiong, Y.: Tinaface: strong but simple baseline for face detection. arXiv preprint arXiv:2011.13183, pp. 1–9 (2020)

  20. Najibi, M., Samangouei, P., Chellappa, R., Davis, L.S.: SSH: Single stage headless face detector. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 4875–4884 (2017)

    Google Scholar 

  21. Liu, Y., Tang, X., Han, J., Liu, J., Rui, D., Wu, X.: Hambox: delving into mining high-quality anchors on face detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 13043–13051 (2020)

    Google Scholar 

  22. Li, J., et al.: ASFD: Automatic and scalable face detector. In: Proceedings of the 29th ACM International Conference on Multimedia, pp. 2139–2147 (2021)

    Google Scholar 

  23. Dong, C., Loy, C.C., He, K., Tang, X.: Image super-resolution using deep convolutional networks. IEEE Trans. Pattern Anal. Mach. Intell. 38(2), 295–307 (2015)

    Article  Google Scholar 

  24. Dong, C., Loy, C.C., Tang, X.: Accelerating the super-resolution convolutional neural network. In: Proceedings of the European Conference on Computer Vision, pp. 391–407 (2016)

    Google Scholar 

  25. Kim, J., Lee, J.K., Lee, K.M.: Accurate image super-resolution using very deep convolutional networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1646–1654 (2016)

    Google Scholar 

  26. Wang, X., et al.: ESRGAN: enhanced super-resolution generative adversarial networks. In: Proceedings of the European Conference on Computer Vision, pp. 36–79 (2018)

    Google Scholar 

  27. Zhang, Y., Li, K., Li, K., Wang, L., Zhong, B., Fu, Y.: Image super-resolution using very deep residual channel attention networks. In: Proceedings of the European Conference on Computer Vision, pp. 286–301 (2018)

    Google Scholar 

  28. Kong, X., Zhao, H., Qiao, Y., Dong, C.: Classsr: a general framework to accelerate super-resolution networks by data characteristic. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 12016–12025 (2021)

    Google Scholar 

  29. Cong, W., et al.: High-resolution image harmonization via collaborative dual transformations. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 18470–18479 (2022)

    Google Scholar 

  30. Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollar, P.: Focal loss for dense object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 2980–2988 (2017)

    Google Scholar 

  31. Zhu, X., Ramanan, D.: Face detection, pose estimation, and landmark localization in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2879–2886 (2012)

    Google Scholar 

  32. Yan, J., Zhang, X., Lei, Z., Li, S.Z.: Face detection by structural models. Image Vis. Comput. 32(10), 790–799 (2014)

    Article  Google Scholar 

  33. Jain, V., Learned-Miller, E.: FDDB: A Benchmark for Face Detection in Unconstrained Settings. Technical Report, UMass Amherst Technical Report (2010)

    Google Scholar 

  34. Yang, S., Luo, P., Loy, C.-C., Tang, X.: Wider face: a face detection benchmark. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5525–5533 (2016)

    Google Scholar 

  35. Zhang, S., Chi, C., Lei, Z., Li, S.Z.: Refineface: refinement neural network for high performance face detection. IEEE Trans. Pattern Anal. Mach. Intell. 43(11), 4008–4020 (2020)

    Article  Google Scholar 

  36. Najibi, M., Singh, B., Davis, L.S.: Fa-rpn: floating region proposals for face detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7723–7732 (2019)

    Google Scholar 

  37. Zhang, S., Wen, L., Shi, H., Lei, Z., Lyu, S., Li, S.Z.: Single-shot scale-aware network for real-time face detection. Int. J. Comput. Vision 127(6), 537–559 (2019)

    Article  Google Scholar 

  38. Zhang, S., Zhu, X., Lei, Z., Shi, H., Wang, X., Li, S.Z.: Faceboxes: a CPU real-time face detector with high accuracy. In: 2017 IEEE International Joint Conference on Biometrics, pp. 1–9 (2017)

    Google Scholar 

  39. Ranjan, R., Patel, V.M., Chellappa, R.: Hyperface: a deep multi-task learning framework for face detection, landmark localization, pose estimation, and gender recognition. IEEE Trans. Pattern Anal. Mach. Intell. 41(1), 121–135 (2017)

    Article  Google Scholar 

  40. Chen, D., Hua, G., Wen, F., Sun, J.: Supervised transformer network for efficient face detection. In: Proceedings of the European Conference on Computer Vision, pp. 122–138 (2016)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computer and Electronic Information, Nanjing Normal University, 210023, Nanjing, China

    Guangtao Wang, Jun Li, Jie Xie & Jianhua Xu

  2. School of Artificial Intelligence, Nanjing University of Information Science and Technology, 210044, Nanjing, China

    Bo Yang

Authors
  1. Guangtao Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jun Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jie Xie
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jianhua Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Bo Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jun Li .

Editor information

Editors and Affiliations

  1. Kyushu Institute of Technology, Kitakyushu, Fukuoka, Japan

    Huimin Lu

  2. The University of Sydney, Sydney, NSW, Australia

    Michael Blumenstein

  3. Yonsei University, Seoul, Korea (Republic of)

    Sung-Bae Cho

  4. Chinese Academy of Sciences, Bejing, China

    Cheng-Lin Liu

  5. Osaka University, Osaka, Ibaraki, Japan

    Yasushi Yagi

  6. Kyushu Institute of Technology, Kitakyushu, Japan

    Tohru Kamiya

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Wang, G., Li, J., Xie, J., Xu, J., Yang, B. (2023). EfficientSRFace: An Efficient Network with Super-Resolution Enhancement for Accurate Face Detection. In: Lu, H., Blumenstein, M., Cho, SB., Liu, CL., Yagi, Y., Kamiya, T. (eds) Pattern Recognition. ACPR 2023. Lecture Notes in Computer Science, vol 14407. Springer, Cham. https://doi.org/10.1007/978-3-031-47637-2_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-47637-2_6

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-47636-5

  • Online ISBN: 978-3-031-47637-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation