Abstract
This paper presents an efficient hybrid DWT-DCT based illumination normalization technique for face recognition. In a face image, illumination usually changes slowly compared to the reflectance except some casting shadows and specularities on the face. Consequently, illumination variations mainly lie in the low frequency band of the face image. Therefore, in the present work, low frequency coefficients are processed to nullify the effect of illumination variations. Discrete wavelet transform (DWT) is used to decompose the image into frequency domain. It is a sub-band coding technique which decomposes image into four sub-bands: low-low (LL), low-high (LH), high-low (HL) and high-high (HH). As illumination is related to low frequency coefficients, normalization is mainly performed on LL sub-band rather than the whole face. The fuzzy filter is applied on the appropriate number of low frequency discrete Cosine transform (DCT) coefficients of LL sub-band to minimize the variations under different lighting conditions. Also, minor corrections are performed on the rest three sub-bands. After modification, the normalized LL sub-band and rest three sub-bands are combined to generate the normalized face image. The given approach achieves zero error rates on Yale B and CMU PIE face database. Also, good performance results have been achieved on Extended Yale B face database. These results clearly confirm the effectiveness of the given approach of illumination normalization.











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References
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
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
Bartlett MS, Movellan JR, Sejnowski TJ (2002) Face recognition by independent component analysis. IEEE Trans Neural Netw 13(6):1450–1464
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
Belhumeur PN, Hespanha JP, Kriegman DJ (1997) Eigenfaces versus Fisherfaces: recognition using class specific linear projection. IEEE Trans Pattern Anal Mach Intell 19(7):711–720
Chellappa R, Wilson CL, Sirohey S (1995) Human and machine recognition of faces: a survey. Proc IEEE 83(5):705–740
Chen HF, Belhumeur PN, Jacobs DW (2000) In search of illumination invariants. Proc IEEE Conf Comput Vis Pattern Recognit 1:254–261
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 B Cybern 36(2):458–466
Cohen A, Ryan RD (1995) Wavelets and multiscale signal processing. Chapman & Hall, London
Cover TM, Hart PE (1967) Nearest neighbor pattern classification. IEEE Trans Inf Theory 13(1):21–27
Delac K, Grgic Kos T (2006) Sub-image homomorphic filtering technique for improving facial identification under difficult illumination conditions. International Conference on Systems, Signals and Image Processing (IWSSIP’06) 19:1519–1524
Etemad K, Chellapa R (1997) Discriminant analysis for recognition of human face images. J Opt Soc Am 14(8):1724–1733
Faraji MR, Qi X (2014) Face recognition under varying illumination with logarithmic fractal analysis. IEEE Signal Processing Letters 21(12):1457–1461. https://doi.org/10.1109/LSP.2014.2343213
Faraji MR, Qi X (2015a) Face recognition under illumination variations based on eight local directional patterns. IET Biometrics 4(1):10–17
Faraji MR, Qi X (2015b) Face recognition under varying illumination based on adaptive homomorphic eight local directional patterns. IET Comput Vis 9(3):390–399
Gao Y, Leung M (2002) Face recognition using line edge map. IEEE Transactions on PAMI 24(6):764–779
Georghiades AS, Belhumeur PN, Jacobs DW (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
Gonzalez RC, Woods RE (2006) Digital image processing. Pearson Education
Jabid T, Kabir M, Chae O (2010) Local directional pattern for face recognition. In: Digest of Technical Papers International Conference on Consumer Electronics 329–330. https://doi.org/10.1109/ICCE.2010.5418801
Jain AK (2006) Fundamentals of digital image processing. Pearson Education
Jobson DJ, Rahman Z, Woodel GA (1997) Properties and performance of a center/surround retinex. IEEE Transactions on Image Processing: special issue on color processing 6:451–462
Juneja K, Verma A and Goel S (2015) An improvement on face recognition rate using local tetra patterns with support vector machine under varying illumination conditions. In: International Conference on Computing, Communication Automation (ICCCA):1079–1084. https://doi.org/10.1109/CCAA.2015.7148566
Kim DJ, Shon MK, Lee S, Kim E (2016) Illumination robust face recognition approach using enhanced preprocessing and feature extraction. J Nanoelectron Optoelectron 11(2):141–147. https://doi.org/10.1166/jno.2016.1855
Klir GJ, Yuan B (1995) Fuzzy sets and fuzzy logic: theory and application. Prentice Hall, New Jersey
Lai Z, Dai D, Ren C, Huang K (2015) Multi scale logarithm difference edge maps for face recognition against varying lighting conditions. IEEE Trans Image Process 24(6):1735–1747. https://doi.org/10.1109/TIP.2015.2409988
Land EH, McCann JJ (1971) Lightness and Retinex theory. J Opt Soc Am 61(1):1–11
Lei Z, Pietikainen M, Li S (2014) Learning discriminant face descriptor. IEEE Trans Pattern Anal Mach Intell 36(2):289–302. https://doi.org/10.1109/TPAMI.2013.112
Li SZ, Jain AK (2005) Handbook of face recognition. Springer
Lin CC, Lin WC (1996) Extracting facial features by PCA. Pattern Recogn 29(12)
MATLAB Reference Manual (2004) http://www.mathworks.com/access/helpdesk/help/toolbox/images
Muller KR, Mika S, Ratsch G, Tsuda K, Scholkopf B (2001) An introduction to Kernel-based learning algorithms. IEEE Trans Neural Netw 12(2)
Phillips PJ, Grother P, Micheals RJ, Blackburn DM, Tabassi E, Bone JM, FRVT (2002) Evaluation report, (2003) [Online]Available: http://www.frvt.org/FRVT2002/
Phillips P J, Scruggs WT, Toole AJO, Flynn PJ, Bowyer KW, Schott CL, Sharpe M (2007) FRVT. (2006) and ICE. (2006) large-scale results. National Institute of Standards and Technology, NISTIR. 7408
Proceedings of the International Conference on audio and video based person authentication. 1997–1999
Proceedings of the International conferences on automatic face and gesture recognition. 1995–1998
Ramirez A, Castillo R, Chae O (2013) Local directional number pattern for face analysis: face and expression recognition. IEEE Trans Image Process 22(5):1740–1752. https://doi.org/10.1109/TIP.2012.2235848
Revathy N, Guhan T (2012) Face recognition system using Back propagation artificial neural networks. IJAET 3(1):321–324
Ronny T, Wanquan V, Svetha V (2004) Application of the DCT energy histogram for face recognition. ICITA 4:305–310
Scholkopf B, Smola AJ, Muller KR (1998) Non-linear component analysis as a kernel eigenvalue problem. Neural Comput 10:1299–1319
Shan S, Gao W, Cao B, Zhao D (2003) Illumination normalization for robust face recognition against varying lighting conditions. IEEE International Workshop on AMFG 157–164
Shashua A, Riklin T (2001) The quotient image: class-based re-rendering and recognition with varying illuminations. IEEE Trans Pattern Anal Mach Intell 23(2):129–139
Sim T, Baker S, Bsat M (2002) The CMU pose, illumination, and expression (PIE) database. Proc. Conf. Automatic Face Gesture Recognition 53–58
Syafeeza AR, Khalil-Hani M, Liew SS, Bakhteri R (2014) Convolutional neural network for face recognition with pose and illumination variation. International Journal of Engineering and Technology (IJET) 6(1):44–57
The Extended Yale Face Database B, available [online]: http://vision.ucsd.edu/~leekc/ExtYaleDatabase/ExtYaleB.html
Turk M, Pentland A (1991) Eigenfaces for recognition. J Cogn Neurosci 3(1):71–96
Veenman CJ, Reinders MJT (2005) The nearest subclass classifier: a compromise between the nearest mean and nearest neighbor classifier. IEEE Trans Pattern Anal Mach Intell 23(9):1417–1429
Vishwakarma VP, Pandey S, Gupta MN (2009) Adaptive histogram equalization and logarithm transform with rescaled low frequency DCT coefficients for illumination normalization. International Journal of Recent Trends in Engineering 1(1):318–322
Vishwakarma VP, Pandey S, Gupta MN (2010) An illumination invariant accurate face recognition with down scaling of DCT coefficients. J Comput Inf Technol 18(1):53–67
Wang B, Li W, Yang W, Liao Q (2011) Illumination normalization based on weber's law with application to face recognition. Signal Processing Letters 18(8):462–465. https://doi.org/10.1109/LSP.2011.2158998
Wu Y, Jiang Y, Zhou Y, Li W, Lu Z, Liao Q (2014) Generalized weber-face for illumination-robust face recognition. Neurocomputing 136:262–267. https://doi.org/10.1016/j.neucom
Xie X, Zheng WS, Lai J, Yuen PC (2011) Face illumination normalization on large and small scale features. IEEE Trans Image Process 20(7):1807–1821
Yale Face Database B (2001) http://cvc.yale.edu/projects/yalefacesB/yalefacesB.html
Zadeh LA (1965) Fuzzy sets. Inf Control 8:338–353
Zhang L, Samaras D. Face recognition under variable lighting using harmonic image exemplars. Proc IEEE Conf Comput Vis Pattern Recognit 1:19–25
Zhang T, Gang B, Yuan Y, Tang YY, Shang Z, Li D, Lang F (2009) Multiscale facial structure representation for face recognition under varying illumination. Pattern Recogn 42(2):251–258
Zhao W, Chellappa R (2001) Symmetric shape-from-shading using self-ratio image. Proceedings in IEEE International Conference on Computer Vision 55–75
Zhao W, Chellappa R, Phillips PJ, Rosenfeld A (2003) Face recognition: a literature survey. ACM Comput Surv 35(4):399–458
Zhao J, Su Y, Wang D, Luo S (2003) Illumination ratio image: synthesizing and recognition with varying illuminations. Pattern Recogn Lett 24:2703–2710
Zhong F, Zhang J (2013) Face recognition with enhanced local directional patterns. Neurocomputing 119:375–384
Zou X, Kittler J, Messer K (2007) Illumination invariant face recognition: a survey. 1st IEEE International Conference on Biometric Theory, Application and Systems 113–120
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Vishwakarma, V.P., Goel, T. An efficient hybrid DWT-fuzzy filter in DCT domain based illumination normalization for face recognition. Multimed Tools Appl 78, 15213–15233 (2019). https://doi.org/10.1007/s11042-018-6837-0
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DOI: https://doi.org/10.1007/s11042-018-6837-0