Local Alignment of Gradient Features for Face Sketch Recognition

  • Ann Theja Alex
  • Vijayan K. Asari
  • Alex Mathew
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7432)

Abstract

Automatic recognition of face sketches is a challenging problem. It has application in forensics. An artist drawn sketch based on the descriptions from the witnesses can be used as the test image to recognize a person from the photo database of suspects. In this paper, we propose a novel method for face sketch recognition. We use the edge features of a face sketch and face photo image to create a feature string called ’edge-string’. The edge-strings of the face photo and face sketch are then compared using the Smith-Waterman algorithm for local alignments. The results on CUHK (Chinese University of Hong Kong) student dataset show the effectiveness of the proposed approach in face sketch recognition.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Kanade, T.: Picture Processing by Computer Complex and Recognition of Human Faces, Technical report, Dept. Information Science, Kyoto Univ. (1973)Google Scholar
  2. 2.
    Brunelli, R., Poggio, T.: Face Recognition: Features versus Templates. IEEE Transactions on Pattern Analysis and Machine Intelligence 15, 1042–1052 (1993)CrossRefGoogle Scholar
  3. 3.
    Gao, Y., Leung, M.K.H.: Face recognition using line edge map. IEEE Transactions on Pattern Analysis And Machine Intelligence 24(6), 764–779 (2002)CrossRefGoogle Scholar
  4. 4.
    Takács, B.: Comparing face images using the modified Hausdorff distance. Pattern Recognition 31, 1873–1881 (1998)CrossRefGoogle Scholar
  5. 5.
    Gao, Y., Leung, M.K.H.: Human face profile recognition using attributed string. Pattern Recognition 35, 353–360 (2002)MATHCrossRefGoogle Scholar
  6. 6.
    Chen, W., Gao, Y.: Recognizing partially occluded faces from a single sample per class using string-based matching. In: Proceedings of the European Conference on Computer Vision, vol. 3, pp. 496–509 (2010)Google Scholar
  7. 7.
    van der Helm, P.A., Leeuwenberg, E.L.J.: Accessibility: a criterion for regularity and hierarchy in visual pattern codes. Journal of Mathematical Psychology 35, 151–213 (1991)MathSciNetMATHCrossRefGoogle Scholar
  8. 8.
    Chechile, R.A., Anderson, J.E., Krafczek, S.A., Coley, S.L.: A syntactic complexity effect with visual patterns: evidence for the syntactic nature of the memory representation. Journal of Experimental Psychology: Learning, Memory, and Cognition 22, 654–669 (1996)CrossRefGoogle Scholar
  9. 9.
    Fu, K.S.: Syntactic pattern recognition and applications. Prentice-Hall, Englewood Cliffs (1982)MATHGoogle Scholar
  10. 10.
    Bunke, H.: Structural and syntactic pattern recognition. In: Chen, C.H., Pau, L.F., Wang, P.S.P. (eds.) Handbook of Pattern Recognition and Computer Vision, pp. 163–209. World Scientific Publishing Company, Singapore (1994)Google Scholar
  11. 11.
    Tang, X., Wang, X.: Face Photo Recognition Using Sketch. Proceedings of IEEE International Conference on Image Processing 1, 257–260 (2002)CrossRefGoogle Scholar
  12. 12.
    Tang, X., Wang, X.: Face Sketch Recognition. IEEE Transactions on Circuits and Systems for Video Technology (CSVT), Special Issue on Image- and Video- Based Biometrics 14(1), 50–57 (2004)Google Scholar
  13. 13.
    Wang, X., Tang, X.: Face Photo-Sketch Synthesis and Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 31, 1955–1967 (2009)CrossRefGoogle Scholar
  14. 14.
    Zhang, W., Wang, X., Tang, X.: Coupled Information-Theoretic Encoding for Face Photo-Sketch Recognition. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 513–520 (2011)Google Scholar
  15. 15.
    Canny, J.: A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 8, 679–698 (1986)CrossRefGoogle Scholar
  16. 16.
    Otsu, N.: A threshold selection method from gray-level histogram. IEEE Transactions on System, Man and Cybernetics SMC-9(1), 62–66 (1979)Google Scholar
  17. 17.
    Huo, Y., Wei, G., Zhang, Y., Wu, L.: An adaptive threshold for the Canny Operator of edge detection, iccsee. In: International Conference on Image Analysis and Signal Processing, pp. 371–374 (2010)Google Scholar
  18. 18.
    Smith, T.F., Waterman, M.S.: Identification of common molecular subsequences. Journal of Molecular Biology 147, 195–197 (1981)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Ann Theja Alex
    • 1
  • Vijayan K. Asari
    • 1
  • Alex Mathew
    • 1
  1. 1.Computer Vision and Wide Area Surveillance Laboratory, Department of Electrical and Computer EngineeringUniversity of DaytonDaytonUSA

Personalised recommendations