The comparison of iris recognition using principal component analysis, independent component analysis and Gabor wavelets

Shi, Jinxin; Gu, Xiaodong

doi:10.1109/iccsit.2010.5563947

Cited by 10 publications

(2 citation statements)

References 3 publications

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“…The PCA represents the classical method for compressing huge data dimensionality to reduced dimensional ones for data analysis, visualization and feature extraction [37]. It uses a procedure mathematically that transforms a number of correlated variables into a smaller number of uncorrelated variables, which is called a principal component which can be used to reconstruct all of the information within a dataset and can be tested to which level attest image couples with an image of a training set.…”

Section: F Principal Component Analysis (Pca)mentioning

confidence: 99%

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Feature Extraction Techniques for Iris Recognition System: A Survey

Adekunle¹,

A²,

Aiyeniko³

et al. 2020

IJIRCST

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The extraction of features involves the method of converting the original pixel values of an image to more meaningful, useful and measurable information that can be used in other techniques, such as image processing, pattern recognition and machine learning. The feature extraction plays a predominant role in iris recognition in which also the recognition rate is determined. The effective recognition accuracy, reduction of misclassification of two iris templates mostly depends on feature extraction techniques. An efficient iris recognition system requires that the discriminating information presents in an iris pattern to be accurately obtained. This paper performed a literature review on different techniques of feature extraction of iris recognition. The recommendation was made on how these techniques can be further enhanced to produce an effective iris recognition system.

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Section: F Principal Component Analysis (Pca)mentioning

confidence: 99%

“…Harsha et al [37] described a distinct method for recognition using iris. The study applied the canny edge detection and a circular Hough transform to find the boundaries of iris in the eye.…”

Section: Related Workmentioning

confidence: 99%