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Face sketch-photo recognition using local gradient checksum: LGCS

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Abstract

A new approach for matching of face sketch images with face photo images and vice versa has been presented here. For the extraction of local edge features from both the sketch and photo images, a new local feature called local gradient checksum (LGCS) has been developed. LGCS is a modality reduction local edge feature on gradient domain. It is calculated as the summation of four pairs of gradient differences between two local pixels that are at 180° with each other. The Euclidean distance between query sketch and gallery of photos are measured depending on extracted LGCS features. To further improve the result, a multi-scale LGCS is proposed. A rank-1 accuracy of 100 % is achieved in a gallery of 606 photos consisting of CUHK, AR, and XM2VTS face dataset. The proposed face sketch-photo recognition system requires neither learning procedures nor training data. Further, the experiment is extended to test the robustness of the proposed algorithm on blurred, noisy and disguised sketches, as well as photos. Under those situations also, LGCS has outperformed center-symmetric local binary pattern, directional local extrema pattern and weber local descriptor feature extraction techniques.

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References

  1. Klare BF, 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 

  2. Jacobs DW, Weinshall D, Gdalyahu Y (2000) Classification with nonmetric distances: image retrieval and class representation. IEEE Trans Pattern Anal Mach Intell 22(6):583–600

    Article  Google Scholar 

  3. Biederman I, Ju G (1988) Surface versus edge-based determinants of visual recognition. J Cognit Psychol 20(1):38–64

    Article  Google Scholar 

  4. Bruce V, Hanna E, Dench N, Healey P, Burton M (1992) The importance of ‘mass’ in line drawings of faces. J Appl Congnit Psychol 6(7):619–628

    Article  Google Scholar 

  5. Zhang T, Yan Y, 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 

  6. Uhl RG Jr, da Lobo NV (1996) A framework for recognizing a facial image from a police sketch. In: Proc Int Conf Comp Vision Pattern Recognit, pp 586–593

  7. Tang X, Wang X (2004) Face Sketch Recognition. IEEE Trans Circt Syst Video Tech 14(1):50–57

    Article  Google Scholar 

  8. Liu Q, Tang X, Jin H, Lu H, Ma S (2005) A nonlinear approach for face sketch synthesis and recognition. In: Proc Int Conf Comp Vision Pattern Recognit, pp 1005–1010

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  11. Chang L, Zhou M, Han Y, Deng X (2010) Face sketch synthesis via sparse representation. In: Proc Int Conf Image Process, pp 2146–2149

  12. Wang N, Gao X, Tao D (2011) Face sketch-photo synthesis under multi-dictionary sparse representation framework. In: Proc Int Conf Image and Graphics, pp 82–87

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  15. Bhatt HS, Bharadwaj S, Singh R, Vatsa M (2010) On matching sketches with digital face images. In: Proc Int Conf Biom Theory Appl Syst, pp 1–7

  16. Zhang W, Wang X, Tang X (2011) Coupled Information-theoretic encoding for face photo-sketch recognition. In: Proc Int Conf Comp Vision Pattern Recognit, pp 513–520

  17. Han H, Klare BF, Bonnen K, Jain AK (2013) Matching composite sketches to face photos: a component-based approach. IEEE Trans Info Forensic Secur 8(1):191–204

    Article  Google Scholar 

  18. Pramanik S, Bhattacharjee D (2012) Geometric Feature Based Face-Sketch Recognition. In: Proc Int Conf Pattern Recognit Info Med Engg, pp 409–415

  19. Rahman A, Beg S (2015) Face sketch recognition using sketching with words. Int J Mach Learn Cyber 6(4):597–605

    Article  Google Scholar 

  20. Ojala T, Pietikäinen M, Mäenpää T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24(7):971–987

    Article  MATH  Google Scholar 

  21. Heikkila M, Pietikainen M, Schmid C (2009) Description of interest regions with local binary patterns. J Pattern Recognit 42(3):425–436

    Article  MATH  Google Scholar 

  22. Murala S, Maheshari R, Balasubramaniun R (2012) Directional local extrema patterns:a new descriptor for content based image retrieval. J Multimed Info Retr 1:191–203

    Article  Google Scholar 

  23. Chen J, Shan S, He C, Zhao G, Pietikäinen M, Chen X, Gao W (2010) WLD: a robust local image descriptor. IEEE Trans Pattern Anal Mach Intell 32(9):1705–1720

    Article  Google Scholar 

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

    Article  Google Scholar 

  25. Forouzan BA (2010) Data communications and networking. 4th ed. Tata McGraw-Hill, New Delhi, India (Special Indian Edition)

  26. He M, Horng S, Fan P, Run R, Chen R, Lai J, Khan MK, Sentosa KO (2010) Performance evaluation of score level fusion in multimodal biometric systems. J. Pattern Recognit 43(5):1789–1800

    Article  MATH  Google Scholar 

  27. Pun C, Lee M (2003) Log-polar wavelet energy signatures for rotation and scale invariant texture classification. IEEE Trans Pattern Anal Mach Intell 25(5):590–603

    Article  Google Scholar 

  28. CUHK Face Sketch database. [Online]. http://mmlab.ie.cuhk.edu.hk/facesketch.html

  29. Martinez AM, Benavente R (1998) The AR face database. CVC Tech Report. p 24

  30. Messer K, Matas J, Kittler J, Luettin J, Maitre G (1999) XM2VTSDB: The Extended of M2VTS Database. In: Proc Int Conf Audio Video Person Auth, pp 72–77

  31. Gong D, Li Z, Liu J, Qiao Y (2013) Multi-feature canonical correlation analysis for face photo-sketch image retrieval. In: ACM International Conference on Multimedia, pp 617–620

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Authors and Affiliations

  1. RCC Institute of Information Technology, Kolkata, West Bengal, 700015, India

    Hiranmoy Roy

  2. Jadavpur University, Kolkata, West Bengal, 70032, India

    Debotosh Bhattacharjee

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  1. Hiranmoy Roy
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  2. Debotosh Bhattacharjee
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Correspondence to Hiranmoy Roy.

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Roy, H., Bhattacharjee, D. Face sketch-photo recognition using local gradient checksum: LGCS. Int. J. Mach. Learn. & Cyber. 8, 1457–1469 (2017). https://doi.org/10.1007/s13042-016-0516-0

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  • DOI: https://doi.org/10.1007/s13042-016-0516-0

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