An effective iris recognition system based on wavelet maxima nad Gabor filter bank

Nabti, Makram; Bouridane, Ahmed

doi:10.1109/isspa.2007.4555544

Cited by 5 publications

(3 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Then filtered image is partitioned into some sub-blocks and features are extracted fro m each block (local extraction). The proposed systems in [27]- [31] are based on the second approach. The effects of the noise factors such as eyelids, eyelashes and reflections in obtained features, can be more reduced using the first approach.…”

Section: Review Of Iris Gabor Filteringmentioning

confidence: 99%

Two Approaches Based on Genetic Algorithm to Generate Short Iris Codes

Ghodrati¹,

Dehghani²,

Danyali³

2012

IJISA

View full text Add to dashboard Cite

This paper has the following contributions in iris recognition compass: first, novel parameters selection for Gabor filters to extract the iris features. Second, due to iris textures randomness and assigning the Gabor parameters by pre-knowledgeable values, traditionally, a large Gabor filter bank has been used to prevent losing the discriminative informat ion. It leads to perform extracting and matching the features heavily and on the other hand, the generated feature vectors are lengthened as required for extra storage space. We have proposed and compared two different approaches based on Genetic Algorithm to reduce the system co mplexity: optimizing the Gabor parameters and feature selection. Third, proposing a novel encoding strategy based on the texture variat ions to generate compact iris codes. The experimental results show that generated iris codes by optimizing the Gabor parameters approach is mo re distinctive and compact than ones based on feature selection approach. Since 2003 he has been on the faculty of the Depart ment of Teleco mmunication Eng ineering, Shiraz University of Technology, Shiraz, Iran. His activities have included Image signal processing, digital filter structures, filter banks, wavelet based signal processing and wireless communication. He is a Member of the IEEE.Habi bol ah Danyali received the B.Sc. and M.Sc. degrees in Electrical Engineering respectively fro m the

show abstract

Section: Review Of Iris Gabor Filteringmentioning

confidence: 99%

Two Approaches Based on Genetic Algorithm to Generate Short Iris Codes

Ghodrati¹,

Dehghani²,

Danyali³

2012

IJISA

View full text Add to dashboard Cite

show abstract

“…The wavelet functions or wavelet analysis is a recent solution for overcoming the shortcomings in image processing, which is crucial for iris recognition. Nabti and Bouridane proposed a novel segmentation method based on wavelet maxima and a special Gabor filter bank for feature extraction, which obtains an efficient recognition with an accuracy of 99.43% [7]. The steps are as follows: the multi-scale edge detection method is used for iris image processing, the extraction of features from an iris-polarized image using the proposed Gabor filter bank, and matching with Hamming distance for identification and recognition.…”

Section: Iris Recognition Algorithms and Principlesmentioning

confidence: 99%

Wavelet-based Feature Extraction Algorithm for an Iris Recognition System

Panganiban¹,

Linsangan²,

Caluyo³

2011

Journal of Information Processing Systems

View full text Add to dashboard Cite

Abstract-The success of iris recognition depends mainly on two factors: image acquisition and an iris recognition algorithm. In this study, we present a system that considers both factors and focuses on the latter. The proposed algorithm aims to find out the most efficient wavelet family and its coefficients for encoding the iris template of the experiment samples. The algorithm implemented in software performs segmentation, normalization, feature encoding, data storage, and matching. By using the Haar and Biorthogonal wavelet families at various levels feature encoding is performed by decomposing the normalized iris image. The vertical coefficient is encoded into the iris template and is stored in the database. The performance of the system is evaluated by using the number of degrees of freedom, False Reject Rate (FRR), False Accept Rate (FAR), and Equal Error Rate (EER) and the metrics show that the proposed algorithm can be employed for an iris recognition system.

show abstract

“…Then filtered image is partitioned into some sub-blocks and features are extracted from each of them. The more details about this category are illustrated in [13][14][15][16][17][18]. Optionally, some authors preferred to encode extracted features in order to decrease burdens of storing the templates.…”

Section: Review Of Iris Gabor Filteringmentioning

confidence: 99%

Iris feature extraction using optimized Gabor wavelet based on multi objective genetic algorithm

Ghodrati

Dehghani

Danyali

2011

2011 International Symposium on Innovations in Intelligent Systems and Applications

View full text Add to dashboard Cite

Iris reputes for its potential to identify the people with high accuracy in large scale. This is not achieved unless the iris patterns are well represented. Gabor filtering is vastly used in iris recognition literature for feature extraction. Conventionally, Gabor parameters value are supplied by pre-knowledgeable values so that the filter bank size is increased to prevent the losing information. In this paper, multi objective genetic algorithm (MOGA) is used to optimize the Gabor-wavelet in order to reduce the filter requirements and increasing the accuracy. The feature vectors are encoded by phase quantization and a novel method based on iris texture variation. Experimental results show recognizing with CRR=99.68% and EER=0.26% for codes with length only 496 bits on a subset including 2125 iris images from CASIA-IrisV3-Interval database.

show abstract

An effective iris recognition system based on wavelet maxima nad Gabor filter bank

Cited by 5 publications

References 7 publications

Two Approaches Based on Genetic Algorithm to Generate Short Iris Codes

Two Approaches Based on Genetic Algorithm to Generate Short Iris Codes

Wavelet-based Feature Extraction Algorithm for an Iris Recognition System

Iris feature extraction using optimized Gabor wavelet based on multi objective genetic algorithm

Contact Info

Product

Resources

About