Skip to main content
SpringerLink
Log in

Face detection based on occlusion area detection and recovery

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Face detection is an important part of face image processing. In many cases, face images have occlusion problems. In this paper, the POOA (positioning the optimal occlusion area) algorithm is proposed for the problem of occlusion face detection. After obtaining the data of the saliency detection processing, firstly, the algorithm computes an average gray value according to the face image, and multiplies the appropriate coefficient as a threshold to obtain a binary image. Then, using the idea of the Haar feature, the two features of “large rectangle” and “large T shape” are used for retrieval, and the occlusion region of the face is obtained by combining the binary images. Finally, a robust principal component analysis (PCA) method is used to obtain the best projection of the occlusion face, and the face occlusion area is filled. The algorithm proposed in this paper is fast. The Adaboost method has achieved good results in terms of occlusion area, size and shape, and the detection precision has also been improved to varying degrees.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. Chan PPK, Liu W, Chen D (2017) Face liveness detection using a flash against 2D spoofing attack. IEEE Trans Inf Forensics Secur 13(2):521–534

    Google Scholar 

  2. Ding G, Wu Q, Yao YD (2013) Kernel-based learning for statistical signal processing in cognitive radio networks. IEEE Signal Process Mag 30(4):126–136

    Google Scholar 

  3. Ding G, Wang J, Wu Q (2015) On the limits of predictability in real-world radio spectrum state dynamics: from entropy theory to 5G spectrum sharing. IEEE Commun Mag 53(7):178–183

    Google Scholar 

  4. Ding G, Wang J, Wu Q (2017) Joint spectral-temporal spectrum prediction from incomplete and corrupted historical observations. IEEE Trans Veh Technol 66(9):8022–8036

    Google Scholar 

  5. Ding G, Wu Q, Zhang L (2017) An amateur drone surveillance system based on cognitive internet of things. IEEE Commun Mag 56(1):29–35

    Google Scholar 

  6. Ding G, Jiao Y, Wang J (2018) Spectrum inference in cognitive radio networks: algorithms and applications. IEEE Communications Surveys & Tutorials, PP(99):1–1

  7. Duan J, Liao S, Guo X (2017) Face detection by aggregating visible components. Computer Vision – ACCV 2016 Workshops, p 319–333

  8. Fu H, Cao X, Tu Z (2013) Cluster-based co-saliency detection. IEEE Trans Image Process 22(10):3766–3778

    MathSciNet  MATH  Google Scholar 

  9. Gen LI, Wenhui LI (2015) Face detection under rotation in image plane using principal direction rotation LBP. Acta Electron Sin 43(1):198–202

    Google Scholar 

  10. Huang QY, Jia CK, Zhang XF (2017) Learning discriminative subspace models for weakly supervised face detection. IEEE Trans Ind Inf 13(6):2956–2964

    Google Scholar 

  11. Ji P, Kim Y, Yang Y (2016) Face occlusion detection using skin color ratio and LBP features for intelligent video surveillance systems. Computer Science and Information Systems, p 253–259

  12. Jin SY, Su H, Stauffer C (2017) End-to-end face detection and cast grouping in movies using Erdös-Rényi clustering. ICCV 2017:5286–5295

    Google Scholar 

  13. LI Jinghui, YE Xueyi, XIA Huyun (2017) AdaBoost face detection algorithm based on the "Big T" Region Software Guide 16(10):22–26

  14. Khan MH, Mcdonagh J, Tzimiropoulos G (2017) Synergy between face alignment and tracking via discriminative global consensus optimization. IEEE International Conference on Computer Vision. Venice, ITALY, p 3811–3819

  15. Li A, Wu Z, Lu H (2018) Collaborative self-regression method with nonlinear feature based on multi-task learning for image classification. IEEE Access 2018:1–1

    Google Scholar 

  16. Li A, Chen D, Wu Z (2018) Self-supervised sparse coding scheme for image classification based on low rank representation. PLoS One 13(6):e0199141

    Google Scholar 

  17. Lin Z, Chen M, Ma Y (2010) The augmented lagrange multiplier method for exact recovery of corrupted low-rank matrices. Eprint Arxiv 9

  18. Lin Y, Wang C, Wang J, Dou Z (2016) A novel dynamic Spectrum access framework based on reinforcement learning for cognitive radio sensor networks. Sensors 16(10):1–22

    Google Scholar 

  19. Lin Y, Zhu X, Zheng Z (2017) The individual identification method of wireless device based on dimensionality reduction and machine learning. J Supercomput (5):1–18

  20. Lin Y, Li Y, Yin X (2018) Multisensor fault diagnosis modeling based on the evidence theory. IEEE Trans Reliab PP(99):1–9

  21. Liu G, Yan S (2012) Active subspace: toward scalable low-rank learning. Neural Comput 24(12):3371–3394

    MathSciNet  MATH  Google Scholar 

  22. Liu X, Wen Z, Zhang Y (2012) Limited memory block Krylov subspace optimization for computing dominant singular value decompositions. SIAM J Sci Comput 35(3):A1641–A1668

    MathSciNet  MATH  Google Scholar 

  23. Liu S, Fu W, He L (2017) Distribution of primary additional errors in fractal encoding method. Multimed Tools Appl 76(4):5787–5802

    Google Scholar 

  24. Liu S, Pan Z, Cheng X (2017) A novel fast fractal image compression method based on distance clustering in high dimensional sphere surface. Fractals 25(04):10

    Google Scholar 

  25. Lv J, Shao X, Xing J (2017) A deep regression architecture with two-stage re-initialization for high performance facial landmark detection. IEEE Conference on Computer Vision and Pattern Recognition. p 3691–3700

  26. Mao X, Xue Y L, Li Z (2009) Robust facial expression recognition based on RPCA and AdaBoost. Image Analysis for Multimedia Interactive Services, p 113–116.

  27. Min R, Hadid A, Dugelay JL (2014) Efficient detection of occlusion prior to robust face recognition. Sci World J 35(3):519–528

    Google Scholar 

  28. Murphy TM, Broussard R, Schultz R (2017) Face detection with a Viola–Jones based hybrid network. IET Biom 6(3):200–210

    Google Scholar 

  29. Pan Z, Liu S, Fu W (2016) A review of visual moving target tracking. Multimed Tools Appl 76(16):16989–17018

    Google Scholar 

  30. Qian J, Yang J, Zhang F (2014) Robust low-rank regularized regression for face recognition with occlusion. IEEE Conference on Computer Vision and Pattern Recognition Workshops, p 21–26.

  31. Tu Y, Lin Y, Wang J (2018) Semi-supervised learning with generative adversarial networks on digital signal modulation classification. CMC-Comput Mater Con 55(2):243–254

    Google Scholar 

  32. Viola P, Jones M (2001) Rapid object detection using a boosted cascade of simple features. CVPR:511–518

  33. Wang W, Zhang C (2018) The influence of ear biometric on profile face detection. Acta Electron Sin 46(3):646–651

    Google Scholar 

  34. Wang J, Cheng Y, Feris RS (2016) Walk and learn: facial attribute representation learning from egocentric video and contextual data. Computer Vision and Pattern Recognition, p 2295–2304

  35. Wu Y, Ji Q (2015) Robust facial landmark detection under significant head poses and occlusion. IEEE International Conference on Computer Vision, p 3658–3666

  36. Wu Y, Wang Z, Ji Q. (2014) A hierarchical probabilistic model for facial feature detection. Computer Vision and Pattern Recognition, p 1781–1788

  37. Wu Q, Li Y, Lin Y (2014) The nonlocal sparse reconstruction algorithm by similarity measurement with Shearlet feature vector. Math Probl Eng 2014(1):1–8

    MathSciNet  MATH  Google Scholar 

  38. Wu Q, Li Y, Lin Y (2016) The application of nonlocal total variation in image denoising for mobile transmission. Multimed Tools Appl 76(16):1–13

    Google Scholar 

  39. Wu Y, Gou C, Ji Q (2017) Simultaneous facial landmark detection, pose and deformation estimation under facial occlusion. IEEE Conference on Computer Vision and Pattern Recognition, p 5719–5728

  40. Yin S, Ouyang P, Dai X (2017) An AdaBoost-based face detection system using parallel configurable architecture with optimized computation. IEEE Syst J 11(1):260–271

    Google Scholar 

  41. Zhang S, Cai Y, Xie M (2013) Face detection based on local region sparse coding. Journal of Software (11):2747–2757

Download references

Acknowledgments

The article is supported by the National Natural Science Foundation of China (Grant No. 61203004).

Author information

Authors and Affiliations

  1. College of Information and Communication Engineering, Harbin Engineering University, Harbin, China

    Yihan Xiao, Dalu Cao & Lipeng Gao

Authors
  1. Yihan Xiao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dalu Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lipeng Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Lipeng Gao.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xiao, Y., Cao, D. & Gao, L. Face detection based on occlusion area detection and recovery. Multimed Tools Appl 79, 16531–16546 (2020). https://doi.org/10.1007/s11042-019-7661-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-019-7661-x

Keywords

Search

Navigation