Skip to main content
Springer Nature Link
Log in

Pixel encoding for unconstrained face detection

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

    We’re sorry, something doesn't seem to be working properly.

    Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

Abstract

In an uncontrolled environment, many of the face detection algorithms lack robustness due to their design. The present research on unconstrained face detection is focused on handcrafted and visual features in isolation. We propose a novel approach to use handcrafted as well as visual features together for improvement in face detection to achieve robustness. The algorithm uses a side-view face detector, which divides the problem space into two: side view face detection and frontal face detection. For frontal faces, Discrete Wavelet Transform (DWT) followed by the encoding of Eyes like landmarks (ELL) pixels is proposed in this work. A Human trait that helps to make decisions even better when they are taken together with the help of more than one decision makers is modeled in this work. To achieve this, a combination of handcrafted and visual features is used. Further to improve classification and to provide a better decision, a faster second stage classification scheme is introduced. The result shows an improvement when handcrafted and visual features are combined instead of using them separately.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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
Fig. 12

Similar content being viewed by others

References

  1. Agrawal AK, Singh YN (2017) An efficient approach for face recognition in uncontrolled environment. Multimed Tools Appl 76(3):3751–3760

    Article  Google Scholar 

  2. Al-Tairi ZH, Rahmat RW, Saripan MI, Sulaiman PS (2014) Skin segmentation using YUV and RGB color spaces. JIPS 10(2):283–299

    Google Scholar 

  3. Bagchi P, Bhattacharjee D, Nasipuri M (2016) A robust analysis, detection and recognition of facial features in 2.5 d images. Multimed Tools Appl 75(18):11059–11096

    Article  Google Scholar 

  4. Berg TL, Berg AC, Edwards J, Forsyth DA (2005) Who’s in the picture. In: Advances in neural information processing systems, pp 137–144

  5. Candemir S, Borovikov E, Santosh K, Antani S, Thoma G (2015) Rsilc: rotation-and scale-invariant, line-based color-aware descriptor. Image Vis Comput 42:1–12

    Article  Google Scholar 

  6. Chen D, Ren S, Wei Y, Cao X, Sun J (2014) Joint cascade face detection and alignment. In: European conference on computer vision. Springer, pp 109–122

  7. Everingham M, Eslami SA, Van Gool L, Williams CK, Winn J, Zisserman A (2015) The pascal visual object classes challenge: a retrospective. Int J Comput Vis 111(1):98–136

    Article  Google Scholar 

  8. Fan W, Bouguila N (2015) Face detection and facial expression recognition using simultaneous clustering and feature selection via an expectation propagation statistical learning framework. Multimed Tools Appl 74(12):4303–4327

    Article  Google Scholar 

  9. Farfade SS, Saberian MJ, Li LJ (2015) Multi-view face detection using deep convolutional neural networks. In: Proceedings of the 5th ACM on International Conference on Multimedia Retrieval. ACM, pp 643–650

  10. Ghiasi G, Fowlkes CC (2014) Occlusion coherence: Localizing occluded faces with a hierarchical deformable part model. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 2385–2392

  11. Gini C (1971) Variability and mutability, contribution to the study of statistical distributions and relations. J Amer Stat Assoc 66:534–544

    Article  Google Scholar 

  12. Girshick R, Donahue J, Darrell T, Malik J (2014) Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 580–587

  13. Haar A (1910) Zur theorie der orthogonalen funktionensysteme. Math Ann 69 (3):331–371

    Article  MathSciNet  Google Scholar 

  14. Hao Z, Liu Y, Qin H, Yan J, Li X, Hu X (2017) Scale-aware face detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 6186–6195

  15. He Q, He B, Zhang Y, Fang H (2019) Multimedia based fast face recognition algorithm of speed up robust features. Multimedia Tools and Applications 78(17):24035–24045

    Article  Google Scholar 

  16. Hong SH, Lee JW, Lama RK, Kwon GR (2016) Real-time face detection and phone-to-face distance measuring for speech recognition for multi-modal interface in mobile device. Multimed Tools Appl 75(12):6717–6735

    Article  Google Scholar 

  17. Huang C, Ai H, Li Y, Lao S (2007) High-performance rotation invariant multiview face detection. IEEE Trans Pattern Anal Mach Intell 29(4):671–686

    Article  Google Scholar 

  18. Hussain MF, Wang H, Santosh K (2018) Gray level face recognition using spatial features. In: International conference on recent trends in image processing and pattern recognition. Springer, pp 216–229

  19. Jain V, Learned-Miller E (2010) FDDB: A benchmark for face detection in unconstrained settings. University of Massachusetts. Amherst Tech Rep UM-CS-2010-009 2(7):8

    Google Scholar 

  20. Jones M, Viola P (2003) Fast multi-view face detection. Mitsubishi Electric Res TR-20003-96 3(14):2

    Google Scholar 

  21. Koestinger M, Wohlhart P, Roth PM, Bischof H (2011) Annotated facial landmarks in the wild: a large-scale, real-world database for facial landmark localization. In: 2011 IEEE international conference on Computer vision workshops (ICCV workshops). IEEE, pp 2144–2151

  22. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097–1105

  23. Li SZ, Zhu L, Zhang Z, Blake A, Zhang H, Shum H (2002) Statistical learning of multi-view face detection. In: European conference on computer vision. Springer, pp 67–81

  24. Li H, Hua G, Lin Z, Brandt J, Yang J (2013) Probabilistic elastic part model for unsupervised face detector adaptation. In: 2013 IEEE international conference on Computer vision (ICCV). IEEE, pp 793–800

  25. Li H, Lin Z, Shen X, Brandt J, Hua G (2015) A convolutional neural network cascade for face detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 5325–5334

  26. Li Y, Sun B, Wu T, Wang Y (2016) Face detection with end-to-end integration of a convnet and a 3D model. In: European conference on computer vision. Springer, pp 420–436

  27. Liao S, Jain AK, Li SZ (2016) A fast and accurate unconstrained face detector. IEEE Trans Pattern Anal Mach Intell 38(2):211–223

    Article  Google Scholar 

  28. Lienhart R, Maydt J (2002) An extended set of haar-like features for rapid object detection. In: International conference on image processing, proceedings, vol 1. IEEE, pp 1–4

  29. Marciniak T, Chmielewska A, Weychan R, Parzych M, Dabrowski A (2015) Influence of low resolution of images on reliability of face detection and recognition. Multimed Tools Appl 74(12):4329–4349

    Article  Google Scholar 

  30. Oyster CW, Haver N (1999) The human eye: structure and function, vol 766. Sinauer Associates, Sunderland

    Google Scholar 

  31. Qin H, Yan J, Li X, Hu X (2016) Joint training of cascaded CNN for face detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 3456–3465

  32. Ranjan R, Patel VM, Chellappa R (2015) A deep pyramid deformable part model for face detection. In: IEEE 7th international conference on Biometrics theory, applications and systems (BTAS), pp 1–8

  33. Ranjan R, Patel VM, Chellappa R (2019) 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

    Article  Google Scholar 

  34. Sawat DD, Hegadi RS, Hegadi RS (2018) Eye like landmarks extraction and patching for face detection using deep neural network. In: International conference on recent trends in image processing and pattern recognition. Springer, pp 395–405

  35. Schroff F, Kalenichenko D, Philbin J (2015) Facenet: a unified embedding for face recognition and clustering. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 815–823

  36. Viola P, Jones M, et al. (2001) Rapid object detection using a boosted cascade of simple features. CVPR (1) 1:511–518

    Google Scholar 

  37. Viola P, Jones MJ (2004) Robust real-time face detection. Int J Comput Vis 57(2):137–154

    Article  Google Scholar 

  38. Wang H, Hussain MF, Mukherjee H, Obaidullah SM, Hegadi RS, Roy K, Santosh K (2018) An empirical study: Elm in face matching. In: International conference on recent trends in image processing and pattern recognition. Springer, pp 277–287

  39. Yang B, Yan J, Lei Z, Li SZ (2014) Aggregate channel features for multi-view face detection. In: IEEE international joint conference on Biometrics (IJCB), pp 1–8

  40. Yang S, Luo P, Loy CC, Tang X (2015) From facial parts responses to face detection: a deep learning approach. In: Proceedings of the IEEE International Conference on Computer Vision, pp 3676–3684

  41. Yang S, Luo P, Loy CC, Tang X (2018) Faceness-net: Face detection through deep facial part responses. IEEE Trans Pattern Anal Mach Intell 40(8):1845–1859

    Article  Google Scholar 

  42. Zhang K, Zhang Z, Li Z, Qiao Y (2016) Joint face detection and alignment using multitask cascaded convolutional networks. IEEE Signal Process Lett 23(10):1499–1503

    Article  Google Scholar 

  43. Zhang S, Zhu X, Lei Z, Shi H, Wang X, Li SZ (2017) S3FD: Single shot scale-invariant face detector. arXiv:1708.05237

  44. Zuiderveld K (1994) Contrast limited adaptive histogram equalization. Graphics Gems IV:474–485. Academic Press Professional, Inc.

    Article  Google Scholar 

Download references

Acknowledgments

Authors thank to the Ministry of Electronics and Information Technology (MeitY), New Delhi for granting Visvesvaraya Ph.D. fellowship through file no. PhD-MLA∖4(34)∖2014-15 Dated: 10/04/2015.

Author information

Authors and Affiliations

  1. Department of computer science, Solapur University, Solapur, 413255, Maharashtra, India

    Dattatray D. Sawat

  2. Department of Computer Science, Indian Institute of Information Technology, Dharwad, 580029, Karnataka, India

    Rajendra S. Hegadi

  3. Information and Communication Technology, University of Malta, Msida, Malta

    Lalit Garg

  4. Department of Computer Science, Central University of Karnataka, Kadaganchi, Kalaburagi–585367, India

    Ravindra S. Hegadi

Authors
  1. Dattatray D. Sawat
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Rajendra S. Hegadi
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Lalit Garg
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Ravindra S. Hegadi
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Dattatray D. Sawat.

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

Sawat, D.D., Hegadi, R.S., Garg, L. et al. Pixel encoding for unconstrained face detection. Multimed Tools Appl 79, 35033–35054 (2020). https://doi.org/10.1007/s11042-020-08800-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-020-08800-1

Keywords