A One Bit Facial Asymmetry Code (FAC) in Fourier Domain for Human Recognition

  • Sinjini Mitra
  • Marios Savvides
  • B. V. K. Vijaya Kumar
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3546)


The present paper introduces a novel set of biometrics based on facial asymmetry measures in the frequency domain using a compact one-bit representation. A simplistic Hamming distance-type classifier is proposed as a means for matching bit patterns for identification purposes which is more efficient than PCA-based classifiers from storage and computation point of view, and produces equivalent results. A comparison with spatial intensity-based asymmetry measures suggests that our proposed measures are more robust to intra-personal distortions with a misclassification rate of only 4.24% on the standard facial expression database (Cohn-Kanade) consisting of 55 individuals. In addition, a rigorous statistical analysis of the matching algorithm is presented. The role of asymmetry of different face parts (e.g., eyes, mouth, nose) is investigated to determine which regions provide the maximum discrimination among individuals under different expressions.


Face Recognition Gesture Recognition Fourier Domain Facial Asymmetry Asymmetry Measure 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Thornhill, R., Gangstad, S.W.: Facial attractiveness. Transactions in Cognitive Sciences 3, 452–460 (1999)CrossRefGoogle Scholar
  2. 2.
    Troje, N.F., Buelthoff, H.H.: How is bilateral symmetry of human faces used for recognition of novel views? Vision Research 38, 79–89 (1998)CrossRefGoogle Scholar
  3. 3.
    Seitz, S.M., Dyer, C.R.: View morphing. In: SIGGRAPH, pp. 21–30 (1996)Google Scholar
  4. 4.
    Gutta, S., Philomin, V., Trajkovic, M.: An investigation into the use of partialfaces for face recognition. In: International Conference on Automatic Face and Gesture Recognition, Washington, D.C, pp. 33–38 (2002)Google Scholar
  5. 5.
    Borod, J.D., Koff, E., Yecker, S., Santschi, C., Schmidt, J.M.: Facial asymmetry during emotional expression: gender, valence and measurement technique. Psychophysiology 36, 1209–1215 (1998)Google Scholar
  6. 6.
    Martinez, A.M.: Recognizing imprecisely localized, partially occluded and expression variant faces from a single sample per class. PAMI 24, 748–763 (2002)Google Scholar
  7. 7.
    Liu, Y., Schmidt, K., Cohn, J., Weaver, R.L.: Human facial asymmetry for expression-invariant facial identification. In: Automatic Face and Gesture Recognition (2002)Google Scholar
  8. 8.
    Liu, Y., Schmidt, K., Cohn, J., Mitra, S.: Facial asymmetry quantification for expression-invariant human identification. CVIU 91, 138–159 (2003)Google Scholar
  9. 9.
    Mitra, S., Liu, Y.: Local facial asymmetry for expression classification. In: Proceedings of CVPR (2004)Google Scholar
  10. 10.
    Kanade, T., Cohn, J.F., Tian, Y.L.: Comprehensive database for facial expression analysis. In: Automatic Face and Gesture Recognition, pp. 46–53 (2000)Google Scholar
  11. 11.
    Hayes, M.H.: The reconstruction of a multidimensional sequence from the phase or magnitude of its fourier transform. ASSP 30, 140–154 (1982)zbMATHCrossRefMathSciNetGoogle Scholar
  12. 12.
    Savvides, M., Vijaya Kumar, B.V.K., Khosla, P.: Face verification using correlation filters. In: 3rd IEEE Automatic Identification Advanced Technologies, Tarrytown, NY, pp. 56–61 (2002)Google Scholar
  13. 13.
    Savvides, M., Kumar, B.V.K.: Eigenphases vs.eigenfaces. In: ICPR (2004)Google Scholar
  14. 14.
    Savvides, M., Kumar, B.V.K., Khosla, P.K.: Corefaces - robust shift invariant PCA based correlation filter for illumination tolerant face recognition. In: CVPR (2004)Google Scholar
  15. 15.
    Oppenheim, A.V., Schafer, R.W.: Discrete-time Signal Processing. Prentice Hall, Englewood Cliffs (1989)zbMATHGoogle Scholar
  16. 16.
    Turk, M.A., Pentland, A.P.: Face recognition using eigenfaces. In: Proceedings of CVPR (1991)Google Scholar
  17. 17.
    Casella, G., Berger, R.L.: Statistical Inference, 2nd edn. Duxbury (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Sinjini Mitra
    • 1
  • Marios Savvides
    • 2
  • B. V. K. Vijaya Kumar
    • 2
  1. 1.Department of StatisticsCarnegie Mellon UniversityPittsburghUSA
  2. 2.Electrical and Computer Engineering DepartmentCarnegie Mellon UniversityPittsburghUSA

Personalised recommendations