Skip to main content
SpringerLink
Log in

Local Centre of Mass Face for face recognition under varying illumination

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

Abstract

In this work we propose a novel method to extract illumination insensitive features for face recognition called local centre of mass face (LCMF). In this LCMF approach the gradient angle between the centre of mass and centre pixel of a selected neighborhood is extracted. Theoretically it is shown that this feature is illumination invariant using the Illumination Reflectance Model (IRM) and is robust to different illumination variations. It is also shown that this method does not involve any explicit computation of Luminance (L) component and as centre of mass is an inherent feature of a mass distribution, its slope with the centre pixel of the neighborhood has local edge preserving capabilities. The angle of the slope obtained using Centre of Mass with the centre pixel of the neighborhood is used as a feature vector. This feature vector is directed from the darkest section of the neighborhood to the brightest section of the neighborhood as Centre of Mass is always positioned towards the brighter side of a mass distribution and hence encrypts the edge orientation. Using the L1 norm distance measure, these feature vectors are used to classify the images. The method does not involve any preprocessing and training of images. The proposed method has been successfully tested under different illumination variant databases like AR, CMU-PIE, and extended Yale B using standard protocols, and performance is compared with recently published methods in terms of rank-1 recognition accuracy. The method is also applied on Sketch-Photo pair database like CUHK. For unbiased or fair performance evaluation, the Sensitivity and Specificity are also being measured for the proposed method on all the databases. The proposed method gives better accuracy performance and outperforms other recent face recognition methods.

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. Ahonen T, Hadid A, Pietikainen M (2006) Face description with local binary patterns: application to face recognition. IEEE Trans Pattern Anal Mach Intell 28(12):2037–2041

    Article  MATH  Google Scholar 

  3. Basri R, Jacobs DW (2003) Lambertian reflectance and linear subspaces. IEEE Trans Pattern Anal Mach Intell 25(2):218–233

    Article  Google Scholar 

  4. Batur AU, Hayes MHIII (2001) Linear subspaces for illumination robust face recognition. In Computer Vision and Pattern Recognition, 2001. CVPR 2001. Proceedings of the 2001 I.E. Computer Society Conference on 2, II-296, IEEE

  5. Belhumeur PN, Kriegman DJ (1998) What is the set of images of an object under all possible illumination conditions? Int J Comput Vis 28(3):245–260

    Article  Google Scholar 

  6. Belhumeur PN, Hespanha JP, Kriegman DJ (1997) Eigenfaces vs. fisherfaces: recognition using class specific linear projection. IEEE Trans Pattern Anal Mach Intell 19(7):711–720

    Article  Google Scholar 

  7. Bhatt H S, Bharadwaj S, Singh R, Vatsa M (2010) September. On matching sketches with digital face images. In Biometrics: Theory Applications and Systems (BTAS), 2010 Fourth IEEE International Conference on (pp. 1–6), IEEE

  8. Bhowmik MK, Bhattacharjee D, Nasipuri M, Basu DK, Kundu M (2010) Quotient based multiresolution image fusion of thermal and visual images using daubechies wavelet transform for human face recognition, international journal of computer science issues (IJCSI). Mauritius 7(3):18–27

    Google Scholar 

  9. Blanz V, Vetter T (2003) Face recognition based on fitting a 3D morphable model. IEEE Trans Pattern Anal Mach Intell 25(9):1063–1074

    Article  Google Scholar 

  10. Brown LG (1992) A survey of image registration techniques. ACM computing surveys (CSUR) 24(4):325–376

    Article  Google Scholar 

  11. Cao X, Shen W, Yu LG, Wang YL, Yang JY, Zhang ZW (2012) Illumination invariant extraction for face recognition using neighboring wavelet coefficients. Pattern Recogn 45(4):1299–1305

    Article  Google Scholar 

  12. Chen HF, Belhumeur PN, Jacobs DW (2000) In search of illumination invariants. In Computer Vision and Pattern Recognition, 2000. Proceedings. IEEE Conference on 1, 254–261. IEEE

  13. Chen W, Er MJ, Wu S (2006a) Illumination compensation and normalization for robust face recognition using discrete cosine transform in logarithm domain. IEEE Trans Syst Man Cyber Part B (Cybernetics) 36(2):458–466

    Article  Google Scholar 

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

    Article  Google Scholar 

  15. Crewe AV, Wall J, Langmore J (1970) Visibility of single atoms. Science 168(3937):1338–1340

    Article  Google Scholar 

  16. Delac K, Grgic M (2004) June. A survey of biometric recognition methods. In Electronics in Marine, 2004. Proceedings Elmar 2004. 46th International Symposium, 184–193, IEEE

  17. Du S, Ward RK (2010) Adaptive region-based image enhancement method for robust face recognition under variable illumination conditions. IEEE Trans Cir Syst Vid Technol 20(9):1165–1175

    Article  Google Scholar 

  18. Duric N (2004) Advanced astrophysics, Cambridge University Press, Cambridge, UK

  19. Faraji MR, Qi X (2014) Face recognition under varying illumination with logarithmic fractal analysis. IEEE Signal Process Lett 21(12):1457–1461

    Article  Google Scholar 

  20. Fattal R, Lischinski D, Werman M (2002) July. Gradient domain high dynamic range compression. In ACM Transactions on Graphics (TOG) (Vol. 21, No. 3, pp. 249–256), ACM

  21. Flynn C, Holmberg J, Portinari L, Fuchs B, Jahreiß H (2006) On the mass-to-light ratio of the local galactic disc and the optical luminosity of the galaxy. Mon Not R Astron Soc 372(3):1149–1160

    Article  Google Scholar 

  22. Ganguly S, Bhattacharjee D, Nasipuri M (2015) Illumination, pose and occlusion invariant face recognition from range images using ERFI model. Intern J Syst Dynamics Appl (IJSDA) 4(2):1–20

    Article  Google Scholar 

  23. Gao Y, Leung MK (2002) Face recognition using line edge map. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(6):764–779

  24. Gao W, Cao B, Shan S, Chen X, Zhou D, Zhang X, Zhao D (2008a) The CAS-PEAL large-scale Chinese face database and baseline evaluations. IEEE Trans Syst Man Cybern-Part A: Syst Humans 38(1):149–161

    Article  Google Scholar 

  25. Gao X, Zhong J, Li J, Tian C (2008b) Face sketch synthesis algorithm based on E-HMM and selective ensemble. IEEE Trans Circuit Syst Video Technol 18(4):487–496

    Article  Google Scholar 

  26. Gao X, Wang N, Tao D, Li X (2012) Face sketch–photo synthesis and retrieval using sparse representation. IEEE Trans. Circuits Syst Video Technol 22(8):1213–1226

    Article  Google Scholar 

  27. 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 

  28. Goh YZ, Teoh ABJ, Goh MKO (2011) Wavelet local binary patterns fusion as illuminated facial image preprocessing for face verification. Expert Syst Appl 38(4):3959–3972

    Article  Google Scholar 

  29. Gonzalez RC, Woods RE (2008) Digital image processing, Prentice Hall, Upper Saddle River, N.J.

  30. Gross R, Brajovic V (2003) June. An image preprocessing algorithm for illumination invariant face recognition. In International Conference on Audio-and Video-Based Biometric Person Authentication, Vol. 2688 of the series Lecture Notes in Computer Science, pp.10–18, Springer Berlin Heidelberg

  31. Guo G, Li S Z, Chan K (2000) Face recognition by support vector machines. In Automatic Face and Gesture Recognition, 2000. Proceedings. Fourth IEEE International Conference on 196–201. IEEE

  32. He X, Yan S, Hu Y, Niyogi P, Zhang HJ (2005) Face recognition using Laplacianfaces. IEEE Trans Pattern Anal Mach Intell 27(3):328–340

    Article  Google Scholar 

  33. Horn B (1986) Robot vision. MIT press, Cambridge

  34. Hurley DJ, Nixon MS, Carter JN (2000) A new force field transform for ear and face recognition. In Image Processing, 2000, Proceedings. 2000 International Conference on 1, 25–28, IEEE

  35. Jain AK, Li SZ (2011) Handbook of face recognition. Springer, New York

    MATH  Google Scholar 

  36. Jain AK, Duin RPW, Mao J (2000) Statistical pattern recognition: a review. IEEE Trans Pattern Anal Mach Intell 22(1):4–37

    Article  Google Scholar 

  37. Jain AK, Pankanti S, Prabhakar S, Hong L, Ross A (2004) August. Biometrics: A grand challenge. In Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on 2, 935–942, IEEE

  38. Jobson DJ, Rahman ZU, Woodell GA (1997) A multiscale retinex for bridging the gap between color images and the human observation of scenes. IEEE Trans Image Process 6(7):965–976

    Article  Google Scholar 

  39. Kanade T (1977) Computer recognition of human faces. Interdiscipl Syst Res 47:1–106 Birkhäuser

    Google Scholar 

  40. Kar A, Bhattacharjee D, Basu D K, Nasipuri M, Kundu M (2013a) An adaptive block based integrated LDP, GLCM, and Morphological features for Face Recognition. arXiv preprint arXiv:1312.1512.

  41. Kar A, Bhattacharjee D, Basu D K, Nasipuri M, Kundu M (2013b) Face Recognition using Hough Peaks extracted from the significant blocks of the Gradient Image. arXiv preprint arXiv:1312.1683

  42. Klare B, Jain AK (2010) August. Heterogeneous face recognition: Matching nir to visible light images. In Pattern Recognition (ICPR), 2010 20th International Conference on IEEE, pp. 1513–1516

  43. Klare BF, Jain AK (2013) Heterogeneous face recognition using kernel prototype similarities. IEEE Trans Pattern Anal Mach Intell 35(6):1410–1422

    Article  Google Scholar 

  44. Klare B, Li Z, Jain AK (2011) Matching forensic sketches to mug shot photos. IEEE Trans Pattern Anal Mach Intell 33(3):639–646

    Article  Google Scholar 

  45. Kundu S (1999) Gravitational clustering: a new approach based on the spatial distribution of the points. Pattern Recogn 32(7):1149–1160

    Article  MathSciNet  Google Scholar 

  46. Lai ZR, Dai DQ, Ren CX, Huang KK (2015) Multiscale logarithm difference edgemaps for face recognition against varying lighting conditions.IEEE. Trans Image Process 24(6):1735–1747

    Article  MathSciNet  Google Scholar 

  47. Land EH, McCann JJ (1971) Lightness and retinex theory. JOSA 61(1):1–11

    Article  Google Scholar 

  48. Lawrence S, Giles CL, Tsoi AC, Back AD (1997) Face recognition: a convolutional neural-network approach. IEEE Trans Neural Netw 8(1):98–113

    Article  Google Scholar 

  49. Lee KC, Ho J, Kriegman D (2001) Nine points of light: Acquiring subspaces for face recognition under variable lighting. In Computer Vision and Pattern Recognition, 2001. CVPR 2001. Proceedings of the 2001 I.E. Computer Society Conference on 1, I-519, IEEE

  50. Lee PH, Wu SW, Hung YP (2012) Illumination compensation using oriented local histogram equalization and its application to face recognition. IEEE Trans Image Process 21(9):4280–4289

    Article  MathSciNet  Google Scholar 

  51. Li B, Jevtic A, Söderström U, Ur Réhman S, Li H (2013) Fast edge detection by center of mass. In The 1st IEEE/IIAE International Conference on Intelligent Systems and Image Processing 2013 (ICISIP2013), (pp. 103–110).

  52. Lian Z, Er MJ, Liang Y (2012) A novel efficient local illumination compensation method based on DCT in logarithm domain. Pattern Recogn Lett 33(13):1725–1733

    Article  Google Scholar 

  53. Lin D, Tang X (2006) May. Inter-modality face recognition. In European Conference on Computer Vision (pp. 13–26), Springer Berlin Heidelberg

  54. Lopez-Molina C, Bustince H, Fernández J, Couto P, De Baets B (2010) A gravitational approach to edge detection based on triangular norms. Pattern Recogn 43(11):3730–3741

    Article  MATH  Google Scholar 

  55. Martinez A M, Benavent, R (1998) “The AR Face Database,” CVC Technical Report #24, June

  56. Messer K, Matas J, Kittler J, Luettin J, Maitre G (1999) March. XM2VTSDB: The extended M2VTS database. In Second international conference on audio and video-based biometric person authentication (Vol. 964, pp. 965–966).

  57. Moghaddam B, Jebara T, Pentland A (2000) Bayesian face recognition. Pattern Recogn 33(11):1771–1782

    Article  Google Scholar 

  58. Ni J, Matsakis P (2010) An equivalent definition of the histogram of forces: theoretical and algorithmic implications. Pattern Recogn 43(4):1607–1617

    Article  MATH  Google Scholar 

  59. Olenick RP, Apostol TM, Goodstein DL (2008) The Mechanical Universe: Introduction to Mechanics and Heat, Cambridge University Press

  60. Pankanti S, Bolle RM, Jain A (2000) Biometrics: the future of identification [guest editors introduction]. Computer 33(2):46–49

    Article  Google Scholar 

  61. Pernkopf F, O'Leary P (2003) Image acquisition techniques for automatic visual inspection of metallic surfaces. NDT & E International 36(8):609–617

    Article  Google Scholar 

  62. Rashedi E, Nezamabadi-Pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci 179(13):2232–2248

    Article  MATH  Google Scholar 

  63. Ren J, Jiang X, Yuan J (2013) Noise-resistant local binary pattern with an embedded error-correction mechanism. IEEE Trans Image Process 22(10):4049–4060

    Article  MathSciNet  Google Scholar 

  64. Rivera AR, Castillo JR, Chae OO (2013) Local directional number pattern for face analysis: face and expression recognition. IEEE Trans Image Process 22(5):1740–1752

    Article  MathSciNet  Google Scholar 

  65. Roy H, Bhattacharjee D (2015) Heterogeneous face matching using geometric edge-texture feature (GETF) and multiple fuzzy-classifier system. Appl Soft Comput 46:967–979

    Article  Google Scholar 

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

    Article  Google Scholar 

  67. Savvides M, Kumar BV (2003) Illumination normalization using logarithm transforms for face authentication. In International Conference on Audio-and Video-Based Biometric Person Authentication, pp. 549–556, June,Springer Berlin Heidelberg

  68. Shan S, Gao W, Cao B, Zhao D (2003) October. Illumination normalization for robust face recognition against varying lighting conditions, In Analysis and Modeling of Faces and Gestures, 2003( AMFG 2003),IEEE International Workshop on 157–164. IEEE

  69. Sharma A, Jacobs DW (2011) June. Bypassing synthesis: PLS for face recognition with pose, low-resolution and sketch. In Computer Vision and Pattern Recognition (CVPR), 2011 I.E. Conference on (pp. 593–600), IEEE

  70. 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 

  71. Sim T, Baker S, Bsat M (2002) May. The CMU pose, illumination, and expression (PIE) database. In Automatic Face and Gesture Recognition, 2002. Proceedings. Fifth IEEE International Conference on (pp. 46–51), IEEE

  72. Štruc V, Pavešić N (2009) September. Illumination invariant face recognition by non-local smoothing, In European Workshop on Biometrics and Identity Management (pp. 1–8), Springer Berlin Heidelberg

  73. Sun G, Liu Q, Liu Q, Ji C, Li X (2007) A novel approach for edge detection based on the theory of universal gravity. Pattern Recogn 40(10):2766–2775

    Article  MATH  Google Scholar 

  74. Tan X, Triggs B (2007) October. Enhanced local texture feature sets for face recognition under difficult lighting conditions. In International Workshop on Analysis and Modeling of Faces and Gestures (pp. 168–182), Springer Berlin Heidelberg

  75. Tan X, Triggs B (2010) Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE Trans Image Process 19(6):1635–1650

    Article  MathSciNet  Google Scholar 

  76. Tang X, Wang X (2004) Face sketch recognition. IEEE Trans Circuits Syst Video Technol 14(1):50–57

    Article  Google Scholar 

  77. Turk M, Pentland A (1991) Eigenfaces for recognition. J Cogn Neurosci 3(1):71–86

    Article  Google Scholar 

  78. Vishwakarma VP, Pandey S, Gupta MN (2009) Adaptive histogram equalization and logarithm transform with rescaled low frequency DCT coefficients for illumination normalization. Intern J Recent Trends Eng 1(1):318–322

    Google Scholar 

  79. Wang X, Tang X (2009) Face photo-sketch synthesis and recognition. IEEE Trans Pattern Anal Mach Intell 31(11):1955–1967

    Article  Google Scholar 

  80. Wang H, Li S Z, Wang Y (2004a) May. Face recognition under varying lighting conditions using self-quotient image. In Automatic Face and Gesture Recognition, 2004. Proceedings. Sixth IEEE International Conference on. 819–824, IEEE

  81. Wang H, Li SZ, Wang, Y (2004b) July. Generalized quotient image. In Computer Vision and Pattern Recognition, 2004. CVPR 2004. Proceedings of the 2004 I.E. Computer Society Conference on 2, 491–498. IEEE

  82. Wang H, Li S Z, Wang Y, Zhang J (2004c) October. Self-quotient image for face recognition. In Image Processing, 2004. ICIP'04. 2004 International Conference on 2, 1397–1400. IEEE

  83. 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 

  84. Wang N, Tao D, Gao X, Li X, Li J (2013) Transductive face sketch-photo synthesis. IEEE Trans Neural Netw Learn Syst 24(9):1364–1376

    Article  Google Scholar 

  85. Wiskott L, Fellous JM, Kuiger N, Von Der Malsburg C (1997) Face recognition by elastic bunch graph matching. IEEE Trans Pattern Anal Mach Intell 19(7):775–779

    Article  Google Scholar 

  86. Wright WE (1977) Gravitational clustering. Pattern Recogn 9(3):151–166

    Article  Google Scholar 

  87. Xie X, Lam KM (2005) Face recognition under varying illumination based on a 2D face shape model. Pattern Recogn 38(2):221–230

    Article  Google Scholar 

  88. Zhang L, Samaras D (2003) June. Face recognition under variable lighting using harmonic image exemplars. In Computer Vision andnnnnn, 2003. Proceedings. 2003 I.E. Computer Society Conference on 1, 1–19, IEEE

  89. 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  Google Scholar 

  90. Zhang W, Wang X, Tang X (2011) June. Coupled information-theoretic encoding for face photo-sketch recognition. In Computer Vision and Pattern Recognition (CVPR), 2011 I.E. Conference on (pp. 513–520), IEEE

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

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Indian Statistical Institute, Kolkata, 700108, India

    Arindam Kar

  2. Department of Computer Science and Engineering, University of Calcutta, Kolkata, India

    Sanchayan Sarkar

  3. Department of Computer Science and Engineering, Jadavpur University, Kolkata, India

    Debotosh Bhattacharjee

Authors
  1. Arindam Kar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sanchayan Sarkar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Debotosh Bhattacharjee
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sanchayan Sarkar.

Additional information

Arindam Kar and Sanchayan Sarkar are both first authors

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kar, A., Sarkar, S. & Bhattacharjee, D. Local Centre of Mass Face for face recognition under varying illumination. Multimed Tools Appl 76, 19211–19240 (2017). https://doi.org/10.1007/s11042-017-4579-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-017-4579-z

Keywords

Search

Navigation