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A novel non-linear modifier for adaptive illumination normalization for robust face recognition

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Abstract

In this paper, a novel approach is presented for adaptive illumination normalization for face recognition under varying illuminations due to change in angle of light projection. Illumination normalization is performed over some of the low frequency discrete Cosine transform (DCT) coefficients which are computed adaptively based upon the significance of alteration of these coefficients. These are, then, modified using a non-linear modifier. The significance of the proposed approach is that the approach is adaptive in normalizing the illumination from the face images as the number of low frequency DCT coefficients that need to be modified using non-linear modifier are selected on the basis of variations of illumination present in the image. This variation in the illumination is also used in developing the non-linear modifier. The proposed approach is important in such a way that the level of illumination variations decides the computations needed i.e. lesser in comparison to the other existing state-of-art approaches as large number of frequency coefficients are not altered. The proposed approach is tested over various face databases: YALE B, Extended YALE B, CMU PIE, AR and YALE face database. The experimental results clearly reveal that the performance of the proposed approach is significantly better than the existing approaches of illumination normalization for face recognition. With the proposed approach, 100% accuracy is attained on all the subsets of YALE B, CMU PIE and on Subset 3 of Extended YALE B database. Promising results are achieved over remaining face databases as well.

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References

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  3. Chen X, Lan X, Liang G et al (2017) Pose-and-illumination-invariant face representation via a triplet-loss trained deep reconstruction model. Multimed Tools Appl 76:22043–22058

    Article  Google Scholar 

  4. Chen Z, Huang W, Lv Z (2017) Towards a face recognition method based on uncorrelated discriminant sparse preserving projection. Multimed Tools Appl 76:17669–17683

    Article  Google Scholar 

  5. Cheng Y, Jiao L, Tong Y et al (2017) Directional illumination estimation sets and multilevel matching metric for illumination-robust face recognition. IEEE Access 5:25835–25845

    Article  Google Scholar 

  6. De Marsico M, Nappi M, Riccio D, Wechsler H (2013) Robust face recognition for uncontrolled pose and illumination changes. IEEE Trans Syst Man Cybern Syst Hum 43:149–163

    Article  Google Scholar 

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

    Article  Google Scholar 

  8. Georghiades A (1997) Yale face database. In: Center for computational vision and control at Yale University. http://cvc.yale.edu/projects/yalefaces/yalefaces.html

  9. 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 23:643–660

  10. Gonzalez R, Woods R (2006) Digital image processing. Pearson Education India, Bengaluru

    Google Scholar 

  11. Huang S-M, Yang J-F (2012) Improved principal component regression for face recognition under illumination variations. IEEE Signal Process Lett 19:179–182

    Article  Google Scholar 

  12. Hui-xian Y, Yong-yong C (2016) Adaptively weighted orthogonal gradient binary pattern for single sample face recognition under varying illumination. IET Biom 5:76–82

    Google Scholar 

  13. Hyvärinen A, Oja E (2000) Independent component analysis: algorithms and applications. Neural Netw 13:411–430

    Article  Google Scholar 

  14. Jaliya UK, Rathod JM (2016) An efficient illumination invariant human face recognition using new preprocessing approach. In: Data mining and advanced computing (SAPIENCE), international conference on, pp 185–190

  15. Kim Y-H, Kim H, Kim S-W et al (2017) Illumination normalisation using convolutional neural network with application to face recognition. Electron Lett 53:399–401

    Article  Google Scholar 

  16. Lee KC, Ho J, Kriegman D (2005) Acquiring linear subspaces for face recognition under variable lighting. IEEE Trans Pattern Anal Mach Intell 27:684–698

    Article  Google Scholar 

  17. Lee P-H, Wu S-W, Hung Y-P (2012) Illumination compensation using oriented local histogram equalization and its application to face recognition. IEEE Trans Image Process 21:4280–4289

    Article  MathSciNet  Google Scholar 

  18. Mansoorizadeh M, Charkari NM (2010) Multimodal information fusion application to human emotion recognition from face and speech. Multimed Tools Appl 49:277–297

    Article  Google Scholar 

  19. Marciniak T, Chmielewska A, Weychan R et al (2015) Influence of low resolution of images on reliability of face detection and recognition. Multimed Tools Appl 74:4329–4349

    Article  Google Scholar 

  20. Martinez AR, Benavente R (1998) The AR face database. Comput Vis Center, Tech Report 24 3:5

  21. McLaughlin N, Ming J, Crookes D (2017) Largest matching areas for illumination and occlusion robust face recognition. IEEE Trans Cybern 47:796–808

    Article  Google Scholar 

  22. Mudunuri SP, Biswas S (2016) Low resolution face recognition across variations in pose and illumination. IEEE Trans Pattern Anal Mach Intell 38:1034–1040

    Article  Google Scholar 

  23. Ochoa-Villegas MA, Nolazco-Flores JA, Barron-Cano O, Kakadiaris IA (2015) Addressing the illumination challenge in two-dimensional face recognition: a survey. IET Comput Vis 9:978–992

    Article  Google Scholar 

  24. Punnappurath A, Rajagopalan AN, Taheri S, Chellappa R, Seetharaman G (2015) Face recognition across non-uniform motion blur, illumination, and pose. IEEE Trans Image Process 24:2067–2082

    Article  MathSciNet  Google Scholar 

  25. Samet H (2008) K-nearest neighbor finding using MaxNearestDist. IEEE Trans Pattern Anal Mach Intell 30:243–252

    Article  Google Scholar 

  26. Savvides M, Kumar BVK (2003) Illumination normalization using logarithm transforms for face authentication. In: International conference on audio-and video-based biometric person authentication, Springer, Berlin, Heidelberg

  27. Sim T, Baker S, Bsat M (2002) The CMU pose, illumination, and expression (PIE) database. In: Automatic face and gesture recognition, 2002. Proceedings. Fifth IEEE international conference on automatic face gesture recognition, pp 53–58

  28. Toth D, Aach T, Metzler V (2000) Illumination-invariant change detection. In: Image analysis and interpretation, 2000. Proceedings. 4th IEEE Southwest Symposium, pp 3–7

  29. Vishwakarma VP (2015) Illumination normalization using fuzzy filter in DCT domain for face recognition. Int J Mach Learn Cybern 6:17–34

    Article  MathSciNet  Google Scholar 

  30. Vishwakarma VP, Goel T (2019) An efficient hybrid DWT-fuzzy filter in DCT domain based illumination normalization for face recognition. Multimed Tools Appl 78:15213–15233

    Article  Google Scholar 

  31. Vishwakarma VP, Pandey S, Gupta MN (2007) A novel approach for face recognition using DCT coefficients re-scaling for illumination normalization. In: Advanced computing and communications, 2007. ADCOM 2007. International conference on, pp 535–539

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

    Google Scholar 

  33. Vishwakarma VP, Pandey S, Gupta MN (2010) An illumination invariant accurate face recognition with down scaling of DCT coefficients. J Comput Inf Technol 18:53–67

    Article  Google Scholar 

  34. Xie X, Zheng W-S, Lai J, Yuen PC, Suen CY (2011) Normalization of face illumination based on large-and small-scale features. IEEE Trans Image Process 20:1807–1821

    Article  MathSciNet  Google Scholar 

  35. Xu X, Liu W, Venkatesh S (2012) An innovative face image enhancement based on principle component analysis. Int J Mach Learn Cybern 3:259–267

    Article  Google Scholar 

  36. Yadav J, Rajpal N, Mehta R (2018) A new illumination normalization framework via homomorphic filtering and reflectance ratio in DWT domain for face recognition. J Intell Fuzzy Syst 35(5):1–13

  37. Yadav J, Rajpal N, Mehta R (2018) An improved hybrid illumination normalisation and feature extraction model for face recognition. Int J Appl Pattern Recognit 5:149–170

    Article  Google Scholar 

  38. Yan C, Xie H, Chen J et al (2018) A fast Uyghur text detector for complex background images. IEEE Trans Multimed 20:3389–3398

    Article  Google Scholar 

  39. Yan C, Tu Y, Wang X et al (2019) STAT: spatial-temporal attention mechanism for video captioning. IEEE Trans Multimed. https://doi.org/10.1109/TMM.2019.2924576

  40. Yan C, Li L, Zhang C et al (2019) Cross-modality bridging and knowledge transferring for image understanding. IEEE Trans Multimed 21(10):2675–2685

  41. Yang J, Zhang D, Frangi AF, Yang J (2004) Two-dimensional PCA: a new approach to appearance-based face representation and recognition. IEEE Trans Pattern Anal Mach Intell 26:131–137

    Article  Google Scholar 

  42. Ye J, Janardan R, Li Q (2005) Two-dimensional linear discriminant analysis. In: Advances in neural information processing systems, pp 1569–1576

  43. Zadeh LA (1965) Fuzzy sets. Inf Control 8:338–353

    Article  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  45. Zhao F, Huang Q, Gao W (2006) Image matching by normalized cross-correlation. In: 2006 IEEE international conference on acoustics speech and signal processing proceedings, pp II 729–II 732

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Correspondence to Sahil Dalal.

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Vishwakarma, V.P., Dalal, S. A novel non-linear modifier for adaptive illumination normalization for robust face recognition. Multimed Tools Appl 79, 11503–11529 (2020). https://doi.org/10.1007/s11042-019-08537-6

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  • DOI: https://doi.org/10.1007/s11042-019-08537-6

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