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Image Processing

Iris recognition with an improved empirical mode decomposition method

[+] Author Affiliations
Jyh-Chian Chang

Kainan University, Department of Computer Science, Taoyuan 338, Taiwan

Ming-Yu Huang

National Defense University, Institute of Technology, Department of Electrical and Electronic Engineering, Taoyuan 335, Taiwan

Jen-Chun Lee

National Defense University, Institute of Technology, Department of Electrical and Electronic Engineering, Taoyuan 335, Taiwan

Chien-Ping Chang

National Defense University, Institute of Technology, Department of Electrical and Electronic Engineering, Taoyuan 335, Taiwan

Te-Ming Tu

National Defense University, Institute of Technology, Department of Electrical and Electronic Engineering, Taoyuan 335, Taiwan

Opt. Eng. 48(4), 047007 (December 26, 2008February 25, 2009February 28, 2009April 22, 2009). doi:10.1117/1.3122322
History: Received December 26, 2008; Revised February 25, 2009; Accepted February 28, 2009; Published April 22, 2009
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With the increasing need for security systems, iris recognition is one of the reliable solutions for biometrics-based identification systems. In general, an iris recognition algorithm includes four basic modules: image quality assessment, preprocessing, feature extraction, and matching. This work presents a whole iris recognition system, but particularly focuses on the image quality assessment and proposes an iris recognition scheme with an improved empirical mode decomposition (EMD) method. First, we assess the quality of each image in the input sequence and select clear enough iris images for the succeeding recognition processes. Then, an improved EMD (IEMD), a multiresolution decomposition technique, is applied to the iris pattern extraction. To verify the efficacy of the proposed approach, experiments are conducted on the public and freely available iris images from the CASIA and UBIRIS databases; three different similarity measures are used to evaluate the outcomes. The results show that the presented schemas achieve promising performance by those three measures. Therefore, the proposed method is feasible for iris recognition and IEMD is suitable for iris feature extraction.

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© 2009 Society of Photo-Optical Instrumentation Engineers

Citation

Jyh-Chian Chang ; Ming-Yu Huang ; Jen-Chun Lee ; Chien-Ping Chang and Te-Ming Tu
"Iris recognition with an improved empirical mode decomposition method", Opt. Eng. 48(4), 047007 (December 26, 2008February 25, 2009February 28, 2009April 22, 2009). ; http://dx.doi.org/10.1117/1.3122322


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Toward accurate and fast iris segmentation for iris biometrics. IEEE Trans Pattern Anal Mach Intell 2009;31(9):1670-84.
Ordinal measures for iris recognition. IEEE Trans Pattern Anal Mach Intell 2009;31(12):2211-26.
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