Using empirical mode decomposition for iris recognition
| Autor: | Ping S. Huang, Jen-Chun Lee, Chien-Ping Chang, Te-Ming Tu, Yu Su |
|---|---|
| Rok vydání: | 2009 |
| Předmět: |
Similarity (geometry)
Biometrics Computer science business.industry Iris recognition Feature extraction ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Pattern recognition Filter (signal processing) Hilbert–Huang transform Wavelet Hardware and Architecture IRIS (biosensor) Computer vision Artificial intelligence business Law Software |
| Zdroj: | Computer Standards & Interfaces. 31:729-739 |
| ISSN: | 0920-5489 |
| DOI: | 10.1016/j.csi.2008.09.013 |
| Popis: | Iris recognition is known as an inherently reliable technique for human identification. Empirical Mode Decomposition (EMD), an adaptive multi-resolution decomposition technique, appears to be suitable for non-linear, non-stationary data analysis. Based on EMD, a fully data-driven method without using any pre-determined filter or wavelet function, an iris recognition scheme is presented by modifying EMD as a low-pass filter to analyze the iris images. To evaluate the efficacy of the proposed approach, three different similarity measures are used. Experimental results show that three metrics have all achieved promising and similar performance. Therefore, the proposed method demonstrates to be feasible for iris recognition and EMD is suitable for feature extraction. |
| Databáze: | OpenAIRE |
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