Principal Component Analysis Based Feature Extraction, Morphological Edge Detection and Localization for Fast Iris Recognition
- 1 Veltech Engineering College, India
- 2 , India
Copyright: © 2020 M. Suganthy and P. Ramamoorthy. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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).
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- Principal Component Analysis (PCA)
- Wavelet Packet Transform (WPT)
- K Nearest Neighbor (KNN)