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Attention-Guided Model for Robust Face Detection System

  • Laksono KurnianggoroEmail author
  • Kang-Hyun JoEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11854)

Abstract

Face detection is a basic computer vision task which is required by various higher level applications including surveillance, authentication, and security system. To satisfy the demand on a high quality face detection method, this paper proposes a robust system based on deep learning model which utilize an attention-based training mechanism. This strategy enables the model to not only predicting the bounding boxes of faces but also outputs a heatmap that corresponds to the existence of faces on a given input image. The proposed method was trained on the most popular face detection dataset and the results show that it produces comparable performance to the existing state of the arts methods.

Keywords

Face detection Deep learning Machine learning Neural network 

Notes

Acknowledgment

This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the ICT Consilience Creative program (IITP-2019-2016-0-00318) supervised by the IITP (Institute for Information & communications Technology Planning & Evaluation).

References

  1. 1.
    Cai, Z., Fan, Q., Feris, R.S., Vasconcelos, N.: A unified multi-scale deep convolutional neural network for fast object detection. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9908, pp. 354–370. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-46493-0_22CrossRefGoogle Scholar
  2. 2.
    Chi, C., Zhang, S., Xing, J., Lei, Z., Li, S.Z., Zou, X.: Selective refinement network for high performance face detection. In: Association for the Advancement of Artificial Intelligence (AAAI) (2019)Google Scholar
  3. 3.
    Dai, J., Li, Y., He, K., Sun, J.: R-FCN: object detection via region-based fully convolutional networks. In: Advances in Neural Information Processing Systems, pp. 379–387 (2016)Google Scholar
  4. 4.
    Deng, J., Guo, J., Zhou, Y., Yu, J., Kotsia, I., Zafeiriou, S.: RetinaFace: single-stage dense face localisation in the wild. arXiv preprint arXiv:1905.00641 (2019)
  5. 5.
    Li, J., et al.: DSFD: dual shot face detector. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2019)Google Scholar
  6. 6.
    Lin, T.Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125 (2017)Google Scholar
  7. 7.
    Liu, W., et al.: SSD: single shot multibox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 21–37. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-46448-0_2CrossRefGoogle Scholar
  8. 8.
    Luo, W., Li, Y., Urtasun, R., Zemel, R.: Understanding the effective receptive field in deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 4898–4906 (2016)Google Scholar
  9. 9.
    Mathias, M., Benenson, R., Pedersoli, M., Van Gool, L.: Face detection without bells and whistles. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8692, pp. 720–735. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-10593-2_47CrossRefGoogle Scholar
  10. 10.
    Najibi, M., Samangouei, P., Chellappa, R., Davis, L.S.: SSH: single stage headless face detector. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4875–4884 (2017)Google Scholar
  11. 11.
    Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016)Google Scholar
  12. 12.
    Redmon, J., Farhadi, A.: Yolo9000: better, faster, stronger. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7263–7271 (2017)Google Scholar
  13. 13.
    Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, pp. 91–99 (2015)Google Scholar
  14. 14.
    Tang, X., Du, D.K., He, Z., Liu, J.: PyramidBox: a context-assisted single shot face detector. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11213, pp. 812–828. Springer, Cham (2018).  https://doi.org/10.1007/978-3-030-01240-3_49 CrossRefGoogle Scholar
  15. 15.
    Viola, P., Jones, M., et al.: Rapid object detection using a boosted cascade of simple features. In: CVPR, vol. 1, no. 1, pp. 511–518 (2001)Google Scholar
  16. 16.
    Wang, J., Yuan, Y., Yu, G.: Face attention network: an effective face detector for the occluded faces. arXiv preprint arXiv:1711.07246 (2017)
  17. 17.
    Yang, B., Yan, J., Lei, Z., Li, S.Z.: Aggregate channel features for multi-view face detection. In: IEEE International Joint Conference on Biometrics, pp. 1–8. IEEE (2014)Google Scholar
  18. 18.
    Yang, S., Luo, P., Loy, C.C., Tang, X.: Wider face: a face detection benchmark. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)Google Scholar
  19. 19.
    Zhang, S., Zhu, X., Lei, Z., Shi, H., Wang, X., Li, S.Z.: S3FD: single shot scale-invariant face detector. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 192–201 (2017)Google Scholar
  20. 20.
    Zhu, C., Tao, R., Luu, K., Savvides, M.: Seeing small faces from robust anchor’s perspective. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5127–5136 (2018)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Graduate School of Electrical EngineeringUniversity of UlsanUlsanSouth Korea

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