An Approach of Iris Feature Extraction for Personal Identification

Iris recognition is one of the most reliable biometric technologies. The performance of an iris recognition system can be undermined by poor quality images and result in high false reject rates (FRR) and failure to enroll (FTE) rates. The selection of the features subset and the classification has b...

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description Iris recognition is one of the most reliable biometric technologies. The performance of an iris recognition system can be undermined by poor quality images and result in high false reject rates (FRR) and failure to enroll (FTE) rates. The selection of the features subset and the classification has become an important issue in the field of iris recognition. In this paper, a wavelet-based quality measure for iris images is proposed. The proposed method includes three modules: image preprocessing, feature extraction and recognition modules. The feature extraction module adopts the wavelet transform as the discriminating features. Similarity between two iris images is estimated using Euclidean distance measures. Features extracted using higher level wavelet decompositions are shown to yield better clustering and higher success rate in recognition.
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source IEEE Electronic Library (IEL) Conference Proceedings
subjects Biometric identification
Biometrics
Educational institutions
Eyelids
Feature extraction
Feature representation
Filters
Humans
Image edge detection
Image recognition
Iris recognition
Wavelet transforms
title An Approach of Iris Feature Extraction for Personal Identification
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