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Heterogeneous Face Matching Using Robust Binary Pattern of Local Quotient: RBPLQ

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Advanced Computing and Systems for Security

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 883))

Abstract

This paper proposes a robust binary scheme for representing and matching near-infrared (NIR)–visible (VIS) and sketch–photo heterogeneous faces. It is termed as robust binary pattern of local quotient (RBPLQ). RBPLQ provides illumination-invariant and noise-resistant features in coarse level. At first, a local quotient (LQ) is extracted for representing illumination-invariant image. Then, a robust local binary feature is proposed to capture the variations of LQ. The proposed technique is applied to different benchmark databases of NIR-VIS and sketch–photo images. Recognition accuracy of 60.72% is achieved in the NIR-VIS database. In the CUFSF database, which is a viewed sketch–photo database, the recognition accuracy of 96.24% is achieved. Extended Yale B database is also tested for verifying the illumination-invariant property of RBPLQ, and it achieved recognition accuracy of 94.20%. Finally, RBPLQ also provides good performance in the case of noisy situations.

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References

  1. Li, S., Chu, R., Liao, S., Zhang, L.: Illumination invariant face recognition using NIR images. IEEE Trans. Pattern Anal. Mach. Intell. 29(4), 627–639 (2007)

    Article  Google Scholar 

  2. Li, S.: Encyclopaedia of Biometrics. Springer (2009)

    Google Scholar 

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

    Article  Google Scholar 

  4. Chen, J., Yi, D., Yang, J., Zhao, G., Li, S., Pietikainen, M.: Learning mappings for face synthesis from near infrared to visual light images. In: Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, pp. 156–163 (2009)

    Google Scholar 

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

    Article  Google Scholar 

  6. Wang, X., Tang, X.: Face photo-sketch synthesis and recognition. IEEE Trans. Pattern Anal. Mach. Intell. 31(1), 1955–1967 (2009)

    Article  Google Scholar 

  7. Li, J., Hao, P., Zhang, C., Dou, M.: Hallucinating faces from thermal infrared images. In: Proceedings IEEE International Conference on Image Processing, pp. 465–468 (2008)

    Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  10. Wang, N., Li, J., Tao, D., Li, X., Gao, X.: Heterogeneous image transformation. Elsevier J. Pattern Recogn. Lett. 34, 77–84 (2013)

    Article  Google Scholar 

  11. Peng, C., Gao, X., Wang, N., Tao, D., Li, X., Li, J.: Multiple representation-based face sketch-photo synthesis. IEEE Trans. Neural Netw. xxx, 1–13 (2016)

    Google Scholar 

  12. Lin, D., Tang, X.: Inter-modality face recognition. In: Proceedings of European Conference on Computer Vision, pp. 13–26 (2006)

    Chapter  Google Scholar 

  13. Sharma, A., Jacobs, D.: Bypassing synthesis: PLS for face recognition with pose, low-resolution and sketch. In: Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, pp. 593–600 (2011)

    Google Scholar 

  14. Yi, D., Liu, R., Chu, R., Lei, Z., Li, S.: Face matching between near infrared and visible light images. In: Proceedings of International Conference on Biometrics, pp. 523–530 (2007)

    Google Scholar 

  15. Lei, Z., Li, S.: Coupled spectral regression for matching heterogeneous faces. In: Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, pp. 1123–1128 (2009)

    Google Scholar 

  16. Mignon, A., Jurie, F.: CMML: a new metric learning approach for cross modal matching. In: Proceedings of Asian Conference on Computer Vision, pp. 1–14 (2012)

    Google Scholar 

  17. Kan, M., Shan, S., Zhang, H., Lao, S., Chen, X.: Multi-view discriminant analysis. IEEE Trans. Pattern Anal. Mach. Intell. 38(1), 188–194 (2016)

    Article  Google Scholar 

  18. Lei, Z., Liao, S., Jain, A.K., Li, S.Z.: Coupled discriminant analysis for heterogeneous face recognition. IEEE Trans. Inf. Forensics Secur. 7(6), 1707–1716 (2012)

    Article  Google Scholar 

  19. Liao, S., Yi, D., Lei, Z., Qin, R., Li, S.: Heterogeneous face recognition from local structure of normalized appearance shared representation learning for heterogeneous face recognition. In: Proceedings of IAPR International Conference on Biometrics (2009)

    Google Scholar 

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

    Article  Google Scholar 

  21. Zhang, W., Wang, X., Tang, X.: Coupled information-theoretic encoding for face photo-sketch recognition. In Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, pp. 513–520 (2011)

    Google Scholar 

  22. Bhatt, H.S., Bharadwaj, S., Singh, R., Vatsa, M.: Memetically optimized MCWLD for matching sketches with digital face images. IEEE Trans. Inf. Forensics Secur. 7(5), 1522–1535 (2012)

    Article  Google Scholar 

  23. Klare, B.F., Jain, A.K.: Heterogeneous face recognition using kernel prototype similarities. IEEE Trans. Pattern Anal. Mach. Intell. 35(6), 1410–1422 (2013)

    Article  Google Scholar 

  24. Zhu, J., Zheng, W., Lai, J., Li, S.: Matching NIR face to VIS face using transduction. IEEE Trans. Inf. Forensics Secur. 9(3), 501–514 (2014)

    Article  Google Scholar 

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

    Google Scholar 

  26. Roy, H., Bhattacharjee, D.: Heterogeneous face matching using geometric edge-texture feature (GETF) and multiple fuzzy-classifier system. Elsevier J. Appl. Soft Comput. 46, 967–979 (2016)

    Article  Google Scholar 

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

    Article  Google Scholar 

  28. Roy, H., Bhattacharjee, D.: Face sketch-photo matching using the local gradient fuzzy pattern. IEEE J. Intell. Syst. 31(3), 30–39 (2016)

    Article  Google Scholar 

  29. Roy, H., Bhattacharjee, D.: A novel quaternary pattern of local maximum quotient for heterogeneous face recognition. Elsevier Pattern Recogn. Lett. (2017). https://doi.org/10.1016/j.patrec.2017.09.029

    Article  Google Scholar 

  30. Roy, H., Bhattacharjee, D.: A novel local wavelet energy mesh pattern (LWEMeP) for heterogeneous face recognition. Elsevier Image Vis. Comput. 72, 1–13 (2018). https://doi.org/10.1016/j.imavis.2018.01.004

    Article  Google Scholar 

  31. Roy, H., Bhattacharjee, D.: A ZigZag pattern of local extremum logarithm difference for illumination-invariant and heterogeneous face recognition. Springer Trans. Comput. Sci. XXXI, 1–19 (2018). https://doi.org/10.1007/978-3-662-56499-8_1

    Google Scholar 

  32. Peng, C., Gao, X., Wang, N., Li, J.: Graphical representation for heterogeneous face recognition. IEEE Trans. Pattern Anal. Mach. Intell. xxx, 1–13 (2016)

    Google Scholar 

  33. Sinha, P., Balas, B., Ostrovsky, Y., Russell, R.: Face recognition by humans: nineteen results all computer vision researchers should know about. In: Proceedings of IEEE, vol. 94 (2006)

    Article  Google Scholar 

  34. Roy, H., Bhattacharjee, D.: Face sketch-photo recognition using local gradient checksum: LGCS. Springer Int. J. Mach. Learn. Cybern. xx (x), 1–13 (2016)

    Google Scholar 

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

    Article  Google Scholar 

  36. Land, E.H., McCann, J.J.: Lightness and Retinex theory. J. Opt. Soc. Am. 61(1), 1–11 (1971)

    Article  Google Scholar 

  37. Horn, B.K.P.: Robot Vision. MIT Press, Cambridge, MA, USA (2011)

    Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  39. Lai, Z., Dai, D., Ren, C., Huang, K.: Multiscale logarithm difference edgemaps for face recognition against varying lighting conditions. IEEE Trans. Image Process. 24(6), 1735–1747 (2015)

    Article  MathSciNet  Google Scholar 

  40. Fan, C.N., Zhang, F.Y.: Homomorphic filtering based illumination normalization method for face recognition. Elsevier J. Pattern Recogn. Lett. 32, 1468–1479 (2011)

    Article  Google Scholar 

  41. An, G., Wu, J., Ruan, Q.: An illumination normalization model for face recognition under varied lighting conditions. Elsevier J. Pattern Recogn. Lett. 31, 1056–1067 (2010)

    Article  Google Scholar 

  42. Wang, H., Li, S., Wang, Y.: Generalized quotient image. In: Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 498–505 (2004)

    Google Scholar 

  43. Belhumeur, P., Georghiades, A., Kriegman, D.: From few to many: Illumination cone models for face recognition under variable lighting and pose. IEEE Trans. Pattern Anal. Mach. Learn. 23(6), 643–660 (2001)

    Article  Google Scholar 

  44. Lee, K.C., Ho, J., Kriegman, D.: Acquiring linear subspaces for face recognition under variable lighting. IEEE Trans. Pattern Anal. Mach. Learn. 27(5), 684–698 (2005)

    Article  Google Scholar 

  45. Li, S., Yi, D., Lei, Z., Liao, S.: The CASIA NIR-VIS 2.0 face database. In: Proceedings IEEE International Workshop on Computer Vision and Pattern Recognition, pp. 348–353 (2013)

    Google Scholar 

  46. Liu, X., Song, L., Wu, X., Tan, T.: Transferring deep representation for NIR-VIS heterogeneous face recognition. In Proceedings of IEEE International Conference on Biometrics (2016)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Information Technology, RCC Institute of Information Technology, Canal South Road, Beliaghata, Kolkata, 700015, India

    Hiranmoy Roy

  2. Department of Computer Science and Engineering, Jadavpur University, Kolkata, 700032, India

    Debotosh Bhattacharjee

Authors
  1. Hiranmoy Roy
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  2. Debotosh Bhattacharjee
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Corresponding author

Correspondence to Hiranmoy Roy .

Editor information

Editors and Affiliations

  1. A.K. Choudhury School of Information Technology, University of Calcutta, Kolkata, West Bengal, India

    Rituparna Chaki

  2. Dipartimento di Scienze Ambientali, Informatica e Statistica, Università Ca’ Foscari, Mestre, Venice, Venezia, Italy

    Agostino Cortesi

  3. Faculty of Computer Science, Bialystok University of Technology, Bialystok, Poland

    Khalid Saeed

  4. Department of Computer Science and Engineering, University of Calcutta, Kolkata, West Bengal, India

    Nabendu Chaki

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Roy, H., Bhattacharjee, D. (2019). Heterogeneous Face Matching Using Robust Binary Pattern of Local Quotient: RBPLQ. In: Chaki, R., Cortesi, A., Saeed, K., Chaki, N. (eds) Advanced Computing and Systems for Security. Advances in Intelligent Systems and Computing, vol 883. Springer, Singapore. https://doi.org/10.1007/978-981-13-3702-4_10

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