Recognizing Human Iris by Modified Empirical Mode Decomposition

  • Jen-Chun Lee
  • Ping S. Huang
  • Te-Ming Tu
  • Chien-Ping Chang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4872)


With the increasing needs in security systems, iris recognition is reliable as one important solution for biometrics-based identification systems. Empirical Mode Decomposition (EMD), a multi-resolution decomposition technique, is adaptive and appears to be suitable for non-linear, non-stationary data analysis. This paper presents an effective approach for iris recognition using the proposed scheme of Modified Empirical Mode Decomposition (MEMD) to analyze the iris signals locally. Since MEMD is a fully data-driven method without using any pre-determined filter or wavelet function, MEMD is used as a low-pass filter to extract the iris features for iris recognition. To verify the efficacy of the proposed approach, three different similarity measures are evaluated. Experimental results show that those three metrics have achieved promising and similar performance. Therefore, the proposed method demonstrates to be feasible for iris recognition and MEMD is suitable for feature extraction.


Biometrics iris recognition Empirical Mode Decomposition (EMD) multi-resolution decomposition 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Jen-Chun Lee
    • 1
  • Ping S. Huang
    • 2
  • Te-Ming Tu
    • 1
  • Chien-Ping Chang
    • 1
  1. 1.Department of Electrical and Electronic Engineering, Institute of Technology, National Defense University, Taoyuan, TaiwanRepublic of China
  2. 2.Department of Electronic Engineering, Ming Chuan University, Taoyuan, TaiwanRepublic of China

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