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Image preprocessing method based on local approximation gradient with application to face recognition

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Abstract

In order to obtain more robust face recognition results, the paper proposes an image preprocessing method based on local approximation gradient (LAG). The traditional gradient is only calculated along 0° and 90°; however, there exist many other directional gradients in an image block. To consider more directional gradients, we introduce a novel LAG operator. The LAG operator is actually calculated by integrating more directional gradients. Because of considering more directional gradients, LAG captures more edge information for each pixel of an image and finally generates an LAG image, which achieves a more robust image dissimilarity between images. An LAG image is normalized into an augmented feature vector using the “z-score” method. The dimensionality of the augmented feature vector is reduced by linear discriminant analysis to yield a low-dimensional feature vector. Experimental results show that the proposed method achieves more robust results in comparison with state-of-the-art methods in AR, Extended Yale B and CMU PIE face database.

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References

  1. Zhao W, Chellappa R, Phillips PJ, Rosenfeld A (2003) Face recognition: a literature survey. ACM Comput Surv 34(4):399–485

    Article  Google Scholar 

  2. Poh N, Chan CH, Kittler J et al (2010) An evaluation of video-to-video face verification. IEEE Trans Inf Forensics Secur 5(4):781–801

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

    Article  Google Scholar 

  4. 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

  5. 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 

  6. Goudelis G, Zafeiriou S, Pantic M (2012) Subspace learning from image gradient orientations. IEEE Trans Pattern Anal Mach Intell 34(12):2454–2466

    Article  Google Scholar 

  7. Goudelis G, Zafeiriou S, Tefas A, Pitas I (2007) Class-specific kernel-discriminant analysis for face verification. IEEE Trans Inf Forensics Secur 2(3):570–587

    Article  Google Scholar 

  8. Yang J, Frangi AF, Yang J, Zhang D, Jin Z (2005) KPCA plus LDA: a complete kernel fisher discriminant framework for feature extraction and recognition. IEEE Trans Pattern Anal Mach Intell 27(2):230–244

    Article  Google Scholar 

  9. Baudat G, Anouar F (2000) Generalized discriminant analysis using a kernel approach. Neural Comput 12:2385–2404

    Article  Google Scholar 

  10. Cevikalp H, Neamtu M, Wilkes M (2006) Discriminative common vector method with kernels. IEEE Trans Neural Netw 17(6):1550–1565

    Article  Google Scholar 

  11. Timo A, Abdenour H, Matti P (2004) Face recognition with Local Binary Patterns. In: Proceedings of the European Conference Computer Vision, Springer, Berlin, pp 269–481

  12. 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 

  13. Chengjun L (2004) Gabor-based kernel PCA with fractional power polynomial models for face recognition. IEEE Trans Pattern Anal Mach Intell 26(5):572–581

    Article  Google Scholar 

  14. Chengjun L, Wechsler H (2002) Gabor feature based classification using the enhanced Fisher linear discriminant model for face recognition. IEEE Trans Image Process 11(4):467–476

    Article  Google Scholar 

  15. Chengjun L, Wechsler H (2003) Independent component analysis of Gabor features for face recognition. IEEE Trans Neural Netw 14(4):919–928

    Article  Google Scholar 

  16. Jobson D, Rahman Z, Woodell G (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 

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

    Article  MathSciNet  Google Scholar 

  18. Wang H, Li S, Wang Y (2004) Face recognition under varying lighting conditions using self quotient image. In: Proc. IEEE Int. Conf. Autom. Face Gesture Recognition, pp 819–824

  19. Gross R, Brajovic V (2003) An image preprocessing algorithm for illumination invariant face recognition. In Proceedings of the AVBPA, pp 10–18

  20. Tan Xiaoyang, Triggs Bill (2010) Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE Trans Image Process 19(6):1635–1649

    Article  MathSciNet  Google Scholar 

  21. 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 

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

    Article  Google Scholar 

  23. Yang M, Zhang L, Yang J Zhang D (2011) Robust sparse coding for face recognition. In Proc. IEEE Int’l Conf. Computer Vision and Pattern Recognition

  24. Elhamifar E, Vidal R (2011) Robust classification suing structured sparse representation. In: Proceedings of the IEEE Int’l ConfComputer Vision and Pattern Recognition

  25. Yang M, Zhang L, Yang J, Zhang D (2013) Regularized robust coding for face recognition. IEEE Trans Image Process 22(5):1753–1766

    Article  MathSciNet  Google Scholar 

  26. Martinez AM, Benavente R (1998) The AR face database. CVC technical report

  27. Lee KC, Ho J, Kriegman DJ (2005) Acquiring linear subspaces for face recognition under variable lighting. IEEE Trans Pattern Anal Mach Intell 27(5):684–698

    Article  Google Scholar 

  28. Sim T, Baker S, Bsat M (2003) The CMU pose, illumination, and expression database[J]. IEEE Trans Pattern Anal Mach Intell 25(12):1615–1618

    Article  Google Scholar 

  29. Guan N, Tao D, Luo Z et al (2012) MahNMF: Manhattan non-negative matrix factorization[J]. arXiv preprint arXiv:1207:3438

  30. Zafeiriou S, Tefas A, Buciu I, Pitas I (2006) Exploiting discriminant information in nonnegative matrix factorization with application to frontal face verification. IEEE Trans Neural Networks 17(3):683–695

    Article  Google Scholar 

  31. Guan Naiyang, Tao Dacheng, Luo Zhigang, Yuan Bo (2011) Manifold regularized discriminative non-negative matrix factorization with fast gradient descent. IEEE Trans Image Process 20(7):2030–2048

    Article  MathSciNet  Google Scholar 

  32. Jain A, Nandakumar K, Ross A (2005) Score normalization in multimodal biometric systems. Pattern Recogn 38(12):2270–2285

    Article  Google Scholar 

  33. Jun Yu, Yong Rui, Yuan Yan Tang, Dacheng Tao (2014) High-order distance-based multiview stochastic learning in image classification. IEEE Trans Cybern 44(12):2431–2442

  34. Jun Yu, Tao Dapeng, Li Jonathan, Cheng Jun (2014) Semantic preserving distance metric learning and applications. Inf Sci 281(10):674–686

    MathSciNet  Google Scholar 

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Authors and Affiliations

  1. School of Computer, Shenyang Aerospace University, Daoyi South 37th Street, Daoyi economic development zone, Shenyang, 110136, Liaoning, China

    Zhaokui Li, Yan Wang & Chunlong Fan

  2. College of Information Engineering, Northwest A&F University, Yangling, 712100, Shanxi, China

    Jinrong He

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  1. Zhaokui Li
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  2. Yan Wang
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  3. Chunlong Fan
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  4. Jinrong He
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Correspondence to Zhaokui Li.

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Li, Z., Wang, Y., Fan, C. et al. Image preprocessing method based on local approximation gradient with application to face recognition. Pattern Anal Applic 20, 101–112 (2017). https://doi.org/10.1007/s10044-015-0470-6

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  • DOI: https://doi.org/10.1007/s10044-015-0470-6

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