Extraction of Illumination-Invariant Features in Face Recognition by Empirical Mode Decomposition

  • Dan Zhang
  • Yuan Yan Tang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5558)


Two Empirical Mode Decomposition (EMD) based face recognition schemes are proposed in this paper to address variant illumination problem. EMD is a data-driven analysis method for nonlinear and non-stationary signals. It decomposes signals into a set of Intrinsic Mode Functions (IMFs) that containing multiscale features. The features are representative and especially efficient in capturing high-frequency information. The advantages of EMD accord well with the requirements of face recognition under variant illuminations. Earlier studies show that only the low-frequency component is sensitive to illumination changes, it indicates that the corresponding high-frequency components are more robust to the illumination changes. Therefore, two face recognition schemes based on the IMFs are generated. One is using the high-frequency IMFs directly for classification. The other one is based on the synthesized face images fused by high-frequency IMFs. The experimental results on the PIE database verify the efficiency of the proposed methods.


Empirical Mode Decomposition Face recognition 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Dan Zhang
    • 1
  • Yuan Yan Tang
    • 1
  1. 1.Department of Computer ScienceHong Kong Baptist UniversityHong Kong SARChina

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