Recognizing Partially Occluded Faces from a Single Exemplar Image Per Person

  • Hamidreza Rashidy Kanan
  • M. Shahram Moin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5576)


Despite remarkable progress on human face recognition, little attention has been given to robustly recognizing partially occluded faces. In this paper, we propose a new approach to recognize partially occluded faces when only one exemplar image per person is available. In this approach, a face image is represented as an array of Patch PCA (PPCA) extracted from a partitioned face image containing information of local regions instead of holistic information of a face. An adaptive weighting technique is utilized to assign proper weights to PPCA features to adjust the contribution of each local region of a face in terms of the richness of identity information and the likelihood of occlusion in a local region. The encouraging experimental results using AR face database demonstrate that the proposed method provides a new solution to the problem of robustly recognizing partially occluded faces in single model databases.


Face Recognition Patch PCA Weighted Matching Partial Occlusion Single Model Database 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Martínez, A.M.: Recognizing Imprecisely Localized, Partially Occluded, and Expression Variant Faces from a Single Sample per Class. IEEE Trans. Pattern Anal. Mach. Intell. 24(6), 748–763 (2002)CrossRefGoogle Scholar
  2. 2.
    Tan, X., Chen, S., Zhou, Z.H., Zhang, F.: Recognizing partially occluded, expression variant faces from single training image per person with SOM and soft k-NN ensemble. IEEE Trans. Neural Networks 16(4), 875–886 (2005)CrossRefGoogle Scholar
  3. 3.
    Martinez, A.M., Benavente, R.: The AR Face Database, CVC Technical Report No. 24, URL (June 1998),
  4. 4.
    Kirby, M., Sirovich, L.: Application of the Karhunen-Loeve Procedure for the Characterization of Human Faces. IEEE Trans. Pattern Analysis and Machine Intelligence 12(1), 103–108 (1990)CrossRefGoogle Scholar
  5. 5.
    Moghaddam, B., Pentland, A.: Probabilistic Visual Learning for Object Representation. IEEE Trans. Pattern Analysis and Machine Intelligence 19(7), 696–710 (1997)CrossRefGoogle Scholar
  6. 6.
    Sirovich, L., Kirby, M.: A Low-Dimensional Procedure for the Characterization of Human Faces. J. Optical Soc. Am. A 4(3), 519–524 (1987)CrossRefGoogle Scholar
  7. 7.
    Turk, M., Pentland, A.: Eigenfaces for Recognition. J. Cognitive Neuroscience 3(1), 71–86 (1991)CrossRefGoogle Scholar
  8. 8.
    Chen, S., Zhu, Y.: Subpattern-based principle component analysis. Pattern Recognition 37(5), 1081–1083 (2004)CrossRefGoogle Scholar
  9. 9.
    Shannon, C.E.: A mathematical theory of communication. Bell Syst. Tech. J. 27, 379-423, 623–656 (1948)Google Scholar
  10. 10.
    Gottumukkal, R., Asari, V.K.: An Improved Face Recognition Technique Based on Modular PCA Approach. Pattern Recognition Letters 25(4), 429–436 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Hamidreza Rashidy Kanan
    • 1
    • 2
  • M. Shahram Moin
    • 2
  1. 1.Electrical and Computer Engineering DepartmentIslamic Azad UniversityQazvinIran
  2. 2.Multimedia Systems Research Group, IT FacultyIran Telecom Research CenterTehranIran

Personalised recommendations