Skip to main content
SpringerLink
Log in

Face recognition against illuminations using two directional multi-level threshold-LBP and DCT

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

Abstract

In this paper, a new approach named as the Two Directional Multi-level Threshold-LBP Fusion (2D–MTLBP-F) is proposed to solve the problem of face recognition against illuminations. The proposed approach utilizes the Threshold Local Binary Pattern (TLBP) in combination with Discrete Cosine Transform (DCT). The utilization of LBP with different thresholds can produce different levels of information, which in turn can be used to improve performance for face recognition against illuminations. First, all images are normalised using a DCT normalisation technique in order to reduce negative effects of noise, blur or illumination. Secondly, the normalised images are transformed into 61 levels of TLBP with thresholds from −30 to 30 and then the normalised DCT image is fused into these TLBP layers as it contains a different type of information in frequency domain. Thirdly, in the training stage, the 2D–MTLBP-F model is trained by searching for the best combination among these 62 layers (61 TLBP +1 DCT image) based on an idea from two dimensional multiple color fusion (2D–MCF). Fourthly, in testing stage for face recognition, all testing and gallery images are transformed into the 2D–MTLBP-F model, and face recognition is performed using the sparse sensing classifier (SRC). Finally, extensive experimental results on five different databases show that the proposed approach has achieved the highest recognition rates in different lighting conditions as well as in uncontrolled environment for FRGC database. In comparison with TLBP and the recently proposed approach of Multi-Scale Logarithm Difference Edge-maps (MSLDE), the proposed approach also achieves much better results on all used datasets.

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
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

References

  1. Adini Y, Moses Y, Ullman S (1997) Face recognition: the problem of compensating for changes in illumination direction. IEEE Trans Pattern Anal Mach Intell 19(7):721–732

    Article  Google Scholar 

  2. Ali A, Hussain S, Haroon F, Hussain S, Khan MF (2012) Face recognition with local binary patterns. Bahria Univ J Inf Commun Technol 5(1):46–50

  3. Alrjebi MM, Liu W, Lee L (2015) Two directional multiple colour fusion for face recognition. In: 2015 International Conference on Computers, Communications, and Systems, IEEE, HongKong

  4. Batur AU, Hayes M (2001) Linear subspaces for illumination robust face recognition. In: CVPR 2001, Proceedings of the 2001 I.E. Computer Society Conference on Computer Vision and Pattern Recognition, 2001, IEEE

  5. Charfeddine M, El’arbi M, Ben Amar C (2014) A new DCT audio watermarking scheme based on preliminary MP3 study. Int J 70(3):1521–1557

    Google Scholar 

  6. Chen W, Meng Joo E, Shiqian W (2006) Illumination compensation and normalization for robust face recognition using discrete cosine transform in logarithm domain. IEEE Trans Syst 36(2):458–466

    Google Scholar 

  7. Chen T, Wotao Yin D, Xiang Sean Zhou TS, Comaniciu TS, Huang TS (2006) Total variation models for variable lighting face recognition. IEEE Trans Pattern Anal Mach Intell 28(9):1519–1524

    Article  Google Scholar 

  8. Chi Ho Chan MA, Tahir J, Kittler M, PietikäInen M (2013) Multiscale local phase quantization for robust component-based face recognition using kernel fusion of multiple descriptors. IEEE Trans Pattern Anal Mach Intell 35(5):1164–1177

    Article  Google Scholar 

  9. Clark R (1994) Destined to be the definitive text: JPEG still image data compression standard. In: Pennebaker W, Mitchell J, Reinhold VN, New York, 1993, 638 pp, pp 815–816, ISBN 0–442–01272-1 (Book Review)

  10. Dasari H, Kraleti Srinivasa R, Chittipotula S (2012) Performance evaluation on the effect of combining DCT and LBP on face recognition system. Int J Mod Educ Comput Sci 4(11):21–32

    Article  Google Scholar 

  11. El Aroussi M, Amine A, Ghouzali S, Rziza M, Aboutajdine D (2008) Combining DCT and LBP feature sets for efficient face recognition. In: 3rd International Conference on Information and Communication Technologies: From Theory to Applications. ICTTA 2008. IEEE, pp 1–6

  12. Etemad K, Chellappa R (1997) Discriminant analysis for recognition of human face images. J Opt Soc Am A 14(8):1724

    Article  Google Scholar 

  13. Faraji MR, Qi X (2015) Face recognition under varying illumination based on adaptive homomorphic eight local directional patterns. IET Comput Vis 9(3):390–399

    Article  Google Scholar 

  14. Feng S (2016) Robust face recognition under varying illumination and occlusion via single layer networks. In: Chinese Conference on Biometric Recognition, Springer

  15. Georghiades AS, Kriegman DJ, Belhurneur P (1998) Illumination cones for recognition under variable lighting: faces. In: Proceedings 1998, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1998, IEEE

  16. Georghiades AS, Belhumeur PN, Kriegman DJ (2001) From few to many: illumination cone models for face recognition under variable lighting and pose. IEEE Trans Pattern Anal Mach Intell 23(6):643–660

    Article  Google Scholar 

  17. Ghatge SS, Dixit VV (2013) Face recognition under varying illumination with local binary pattern. Int J Adv Res Electr Electron Instrum Eng 2(2):772–779

    Google Scholar 

  18. Gross R, Matthews I, Cohn J, Kanade T, Baker S (2008) Multi-Pie. In: 8th IEEE International Conference on Automatic Face & Gesture Recognition, pp 1–8

  19. Jeyhoon M, Asgari M, Ehsan L, Jalilzadeh S (2017) Blind audio watermarking algorithm based on DCT, linear regression and standard deviation. Int J 76(3):3343–3359

    Google Scholar 

  20. Jie Chen M, Shiguang Shan M, Chu He M, Guoying Zhao M (2010) WLD: a robust local image descriptor. IEEE Trans Pattern Anal Mach Intell 32(9):1705–1720

    Article  Google Scholar 

  21. Kan, M, Shan, S, Chang H, Chen X (2014) Stacked Progressive Auto-Encoders (SPAE) for face recognition across poses. In: 2014 I.E. Conference on Computer Vision nd Pattern Recognition (CVPR). IEEE Computer Society, Washington, DC, pp 1883–1890

  22. Kondo T, Yan H (1999) Automatic human face detection and recognition under non-uniform illumination. Pattern Recogn 32(10):1707–1718

    Article  Google Scholar 

  23. Lai Z-R, Dai D-Q, Ren C-X, Huang K-K (2015) Multiscale logarithm difference edgemaps for face recognition against varying lighting conditions. IEEE Trans Image Process Publ IEEE Signal Process Soc 24(6):1735

    MathSciNet  Google Scholar 

  24. Li B, An S, Liu W, Krishna A (2011) The MCF model: utilizing multiple colours for face recognition. In: Sixth International Conference on Images and Graphics (ICIG). https://doi.org/10.1109/ICIG.2011.109

  25. Li B, Liu W-Q, An S, Krishna A (2014) Robust face recognition by utilizing colour information and sparse representation. Int J Pattern Recognit Artif Intell 28. https://doi.org/10.1142/S0218001414560047

  26. Liu C, Dai D, Yan H (2007) Local discriminant wavelet packet coordinates for face recognition. J Mach Learn Res 8:1165–1195

    MATH  Google Scholar 

  27. Martinez AM, Benavente R (1998) The AR face database. CVC Technical Report #24

  28. Meng J, Gao Y, Wang X, Lin T, Zhang J (2010) Face recognition based on local binary patterns with threshold. In: IEEE International Conference on Granular Computing, San Jose, CA, pp 352–356

  29. Pathirage C, Li L, Liu W-Q, Zhang M (2015) Stacked face de-noising auto encoders for expression-robust face recognition. In: 2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA). https://doi.org/10.1109/DICTA.2015.737131

  30. Perez CA, Castillo LE (2009) Illumination compensation for face recognition by genetic optimization of the self-quotient Image method. In: International Symposium on Optomechatronic Technologies, pp 322–327

  31. Phillips PJ, Flynn PJ, Scruggs T, Bowyer KW, Chang J, Hoffman K, Marques J, Min J, Worek W (2005) Overview of the face recognition grand challenge. In: IEEE computer society conference on Computer Vision and Pattern Recognition (CVPR 2005), Vol 1. EEE Computer Society Washington, DC, USA, pp 947–954

  32. Ramirez Rivera A, Rojas Castillo J, Oksam Chae J (2013) Local directional number pattern for face analysis: face and expression recognition. IEEE Trans Image Process 22(5):1740–1752

    Article  MathSciNet  MATH  Google Scholar 

  33. Rao KR (1990) Discrete cosine transform: algorithms, advantages, applications. In: Rao KR, Yip P (ed) Academic Press, Boston

  34. Roy H, Bhattacharjee D (2016) Local-gravity-face (lg-face) for illumination-invariant and heterogeneous face recognition. IEEE Trans Inf Forensics Secur 11(7):1412–1424

    Article  Google Scholar 

  35. Shashua A, Riklin-Raviv T (2001) The quotient image: class-based re-rendering and recognition with varying illuminations. IEEE Trans Pattern Anal Mach Intell 23(2):129–139

    Article  Google Scholar 

  36. Shan S, Gao W, Cao B, Zhao D (2003) Illumination normalization for robust face recognition against varying lighting conditions. In:Proceedings of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures. EEE Computer Society, Washington, DC, pp 157–164

  37. Shrivastava N, Tyagi V (2016) An integrated approach for image retrieval using local binary pattern. Int J 75(11):6569–6583

    Google Scholar 

  38. Sun Y, Wang X, Tang X (2014) Deep learning face representation by joint identification-verification. In: Proceedings of the 27th International Conference on Neural Information Processing Systems - Volume 2, Montreal, Canada. MIT Press, Cambridge

  39. Sun Y, Liang D, Wang X, Tang X (2015) Deepid3: face recognition with very deep neural networks. arXiv preprint arXiv:1502.00873

  40. Wang H, Li SZ, Wang Y (2004) Face recognition under varying lighting conditions using self quotient image. In: Sixth IEEE International Conference on Automatic Face and Gesture Recognition

  41. Wang B, Li W, Yang W, Liao Q (2011) Illumination normalization based on Weber's law with application to face recognition. IEEE Signal Process Lett 18(8):462–465

    Article  Google Scholar 

  42. Wen Y, Zhang K, Li Z, Qiao Y (2016) A discriminative feature learning approach for deep face recognition. In: European Conference on Computer Vision. Springer, Cham

  43. Wright J, Yang AY, Ganesh A, Sastry SS, Yi Ma SS (2009) Robust face recognition via sparse representation. IEEE Trans Pattern Anal Mach Intell 31(2):210–227

    Article  Google Scholar 

  44. Xiaohua Xie PC, Wei-Shi Zheng PC, Jianhuang Lai PC, Yuen PC (2008) Face illumination normalization on large and small scale features. In: IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, AK, USA, pp 1–8

  45. Yang B, Chen S (2013) A comparative study on Local Binary Pattern (LBP) based face recognition: LBP histogram versus LBP image. Neurocomputing 120:365–379

    Article  Google Scholar 

  46. Zhang T, Tang YY, Fang B, Shang Z, Liu X (2009) Face recognition under varying illumination using gradientfaces. IEEE Trans Image Process 18(11):2599–2606

    Article  MathSciNet  MATH  Google Scholar 

  47. Zhao X et al (2015) A wavelet-based image preprocessing method or illumination insensitive face recognition. J Inf Sci Eng 31(5):1711–1731

    Google Scholar 

  48. Zhong F, Zhang J (2013) Face recognition with enhanced local directional patterns. Neurocomputing 119:375–384

    Article  Google Scholar 

  49. Zhou E, Cao Z, Yin Q (2014) Naive-deep face recognition: touching the limit of lfw benchmark or not? ArXiv preprint arXiv. 1501

  50. Zhou L, Liu W, Lu Z-M, Nie T (2014) Face recognition based on curvelets and local binary pattern features via using local property preservation. J Syst Softw 95:209–216

    Article  Google Scholar 

  51. Zhu Z, Luo P, Wang X, Tang X (2014) Deep learning multi-view representation for face recognition. arXiv preprint arXiv:1406.6947

Download references

Author information

Authors and Affiliations

  1. Department of Computing, Curtin University, Bentley, WA, 6102, Australia

    Mustafa M. Alrjebi, Wanquan Liu & Ling Li

Authors
  1. Mustafa M. Alrjebi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wanquan Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ling Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mustafa M. Alrjebi.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Alrjebi, M.M., Liu, W. & Li, L. Face recognition against illuminations using two directional multi-level threshold-LBP and DCT. Multimed Tools Appl 77, 25659–25679 (2018). https://doi.org/10.1007/s11042-018-5812-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-018-5812-0

Keywords

Search

Navigation