Efficient Iris Recognition through Improvement of Feature Vector and Classifier

By Shinyoung Lim, Kwanyong Lee, O. Byeon, Taiyun Kim. 2001

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How researchers see this paper

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Efficient iris recognition by characterizing key local variations (2004, methodology): “Lim et al. [15] decomposed an iris image into four levels using 2-”
Comparison and combination of iris matchers for reliable personal authentication (2010, methodology): “feature extraction using the four-level Haar wavelet decomposition [10] of the”
Personal Identification Based on Iris Texture Analysis (2003, background): “3, the second image is out of focus, the third one contains many noticeable interlacing lines (especially in regions close to the boundary) caused by eye motion, and the last one is an example of severe occlusions by eyelids and eyelashes.”
Recognition of Human Iris Patterns for Biometric Identification (2003, methodology): “[9] also use the wavelet transform to extract features from the iris region.”
Ordinal Measures for Iris Recognition (2009, background): “" Many iris recognition methods [1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17] have been reported, but these questions remain open and intriguing.”