Iris recognition by fusing different representations of multi-scale Taylor expansion

Bastys, Algirdas; Kranauskas, Justas; Krüger, Volker

doi:10.1016/j.cviu.2011.02.004

Cited by 24 publications

(7 citation statements)

References 34 publications

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“…Bastys, Kranauskas, and Kruger have proposed iris recognition system where score level fusion of shreds of evidence collected from iris using phase-based, local extremum has compared with individual ones; this combination has given better results than iris recognition without integration [6]. The Haar wavelet features and Gabor features were integrated at score level in [7] and shown a better result than unimodal systems.…”

Section: Introductionmentioning

confidence: 99%

Designing Efficient Feature Space Reduction Schemes for Multi-Algorithmic Iris Recognition System based on Feature Level Fusion of Texture and Phase Features

Kumari*¹,

Suma²

2019

IJRTE

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Iris recognition system has gained prominent focus because of its uniqueness, stability over time. But the recognition level of single biometric based recognition systems is greatly affected by environmental conditions, physiological deficiency. Multi-biometric systems diminish this problem with the fusion of features collected from various traits or samples of the same trait, a single trait by employing multiple algorithms or multiple instances. To gain the advantages of multi-biometric systems in iris recognition, a Multi-algorithmic iris recognition system has been proposed where Texture features from iris are extracted by using 2D-Log Gabor filter and Phase features are extracted by Haar Wavelet; And these features can be integrated at various levels like Decision, Rank, Score, feature, and pixel. Even though the feature level fusion contains rich information about biometric samples when compared to remaining fusion levels; it involves mapping complexity, high dimensional feature space. To gain advantage of feature level fusion in iris recognition and to overcome the problem of resulted high dimensional feature space, Genetic Algorithm (GA) based reduction scheme, Principal Component Analysis (PCA) reduction strategy and a hybrid reduction scheme which is a combination of PCA and GA have been applied to reduce the resulted feature space. The performance of these reduction strategies have evaluated on CASIA iris database, IIT Delhi iris database using Machine Learning approaches. The results have shown that the feature space has dramatically reduced while keeping recognition accuracy and also revealed that space and time requirements have significantly decreased after employing feature reduction schemes.

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Section: Introductionmentioning

confidence: 99%

Designing Efficient Feature Space Reduction Schemes for Multi-Algorithmic Iris Recognition System based on Feature Level Fusion of Texture and Phase Features

Kumari*¹,

Suma²

2019

IJRTE

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“…Chen et al (Chen et al, 2006) and Proenca and Alexandre (Proenca and Alexandre, 2007) have proposed the technique of dividing the unwrapped candidate iris into N divisions and obtained the multiple signatures for classification. A modified version of it has been used in (Barpanda et al, 2015), and in (Bastys et al, 2011) and (Pillai et al, 2011) authors have also utilized sector divisions of iris region for noisy and uncooperative iris recognition.…”

Section: Related Workmentioning

confidence: 99%

“…The ground truth of the iris image is : the upper and lower region are usually occluded by the eyelids. In (Bastys et al, 2011) Hence, we have utilised the statistical properties of the patches to classify them as best iris region and not so best iris region. The probability density function of each patch is separately computed.…”

Section: Multi-patches Techniquementioning

confidence: 99%

Multi-Patches Iris Based Person Authentication System Using Particle Swarm Optimization and Fuzzy C-Means Clustering

Shekar

Bhat²

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Locating the boundary parameters of pupil and iris and segmenting the noise free iris portion are the most challenging phases of an automated iris recognition system. In this paper, we have presented person authentication frame work which uses particle swarm optimization (PSO) to locate iris region and circular hough transform (CHT) to device the boundary parameters. To undermine the effect of the noise presented in the segmented iris region we have divided the candidate region into N patches and used Fuzzy c-means clustering (FCM) to classify the patches into best iris region and not so best iris region (noisy region) based on the probability density function of each patch. Weighted mean Hammimng distance is adopted to find the dissimilarity score between the two candidate irises. We have used Log-Gabor, Riesz and Taylor's series expansion (TSE) filters and combinations of these three for iris feature extraction. To justify the feasibility of the proposed method, we experimented on the three publicly available data sets IITD, MMU v-2 and CASIA v-4 distance.

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“…Representation of the gating network integration method patterns, personal most reliable techniques [1][2][3][4].…”

Section: … (Patricia Melin)mentioning

confidence: 99%

“…The recognition of persons using biometrics is a problem that has been considered by many researchers [1][2][3][4]. Biometrics plays an important role in public safety and to accurately identify each individual to distinguish them from each other [5].…”

Section: Introductionmentioning

confidence: 99%

Genetic Optimization of Neural Networks for Person Recognition Based on the Iris

et al. 2012

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Abstrak Kata kunci: algoritma genetik, biometrik iris, fuzi, jaringan syaraf tiruan, optimasi Abstract This paper describes the application of modular neural network architectures for person recognition using the human iris image as a biometric measure. The iris database was obtained from the Institute of Automation of the Academy of Sciences China (CASIA). We show simulation results with the modular neural network approach, its optimization using genetic algorithms, and the integration with

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Iris recognition by fusing different representations of multi-scale Taylor expansion

Cited by 24 publications

References 34 publications

Designing Efficient Feature Space Reduction Schemes for Multi-Algorithmic Iris Recognition System based on Feature Level Fusion of Texture and Phase Features

Designing Efficient Feature Space Reduction Schemes for Multi-Algorithmic Iris Recognition System based on Feature Level Fusion of Texture and Phase Features

Multi-Patches Iris Based Person Authentication System Using Particle Swarm Optimization and Fuzzy C-Means Clustering

Genetic Optimization of Neural Networks for Person Recognition Based on the Iris

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