Principal Component Analysis Based Feature Extraction, Morphological Edge Detection and Localization for Fast Iris Recognition

This study involves the Iris Localization based on morphological or set theory which is well in shape detection. Principal Component Analysis (PCA) is used for preprocessing, in which the removal of redundant and unwanted data is done. Applications such as Median Filtering and Adaptive thresholding...

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Bibliographic Details
Published inJournal of computer science Vol. 8; no. 9; pp. 1428 - 1433
Main Authors Suganthy, M, Ramamoorthy, P
Format Journal Article
LanguageEnglish
Published 2012
Subjects
Algorithms
Filtering
Lighting
Localization
Materials handling
Position (location)
Principal component analysis
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Summary:This study involves the Iris Localization based on morphological or set theory which is well in shape detection. Principal Component Analysis (PCA) is used for preprocessing, in which the removal of redundant and unwanted data is done. Applications such as Median Filtering and Adaptive thresholding are used for handling the variations in lighting and noise. Features are extracted using Wavelet Packet Transform (WPT). Finally matching is performed using KNN. The proposed method is better than the previous method and is proved by the results of different parameters. The testing of the proposed algorithm was done using CASIA iris database (V1.0) and (V3.0).
Bibliography:ObjectType-Article-1
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ISSN:1549-3636
1552-6607
DOI:10.3844/jcssp.2012.1428.1433