Facial Asymmetry in Frequency Domain: The “Phase” Connection

  • Sinjini Mitra
  • Marios Savvides
  • B. V. K. Vijaya Kumar
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3776)


Facial asymmetry has now been established as a useful biometric for human identification in the presence of expression variations ([1]). The current paper investigates an alternative representation of asymmetry in the frequency domain framework, and its significance in identification tasks in terms of the phase component of the frequency spectrum of an image. The importance of the latter in face reconstruction is well-known in the engineering literature ([2]) and this establishes a firm ground for the success of asymmetry as a potentially useful biometric. We also point out some useful implications of this connection and dual representation. Moreover, the frequency domain features are shown to be more robust to intra-personal distortions than the corresponding spatial measures and yield error rates as low as 4% on a dataset with images showing extreme expression variations.


Frequency Domain Face Recognition Face Image Gesture Recognition Phase Component 
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.


  1. 1.
    Liu, Y., Schmidt, K., Cohn, J., Mitra, S.: Facial asymmetry quantification for expression-invariant human identification. CVIU 91, 138–159 (2003)Google Scholar
  2. 2.
    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
  3. 3.
    Thornhill, R., Gangstad, S.W.: Facial attractiveness. Transactions in Cognitive Sciences 3, 452–460 (1999)CrossRefGoogle Scholar
  4. 4.
    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
  5. 5.
    Seitz, S.M., Dyer, C.R.: View morphing. SIGGRAPH, 21–30 (1996)Google Scholar
  6. 6.
    Gutta, S., Philomin, V., Trajkovic, M.: An investigation into the use of partial-faces for face recognition. In: International Conference on Automatic Face and Gesture Recognition, Washington D.C., pp. 33–38 (2002)Google Scholar
  7. 7.
    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
  8. 8.
    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
  9. 9.
    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
  10. 10.
    Mitra, S., Liu, Y.: Local facial asymmetry for expression classification. In: Proceedings of CVPR (2004)Google Scholar
  11. 11.
    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
  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: CVPR (1991)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