Iris recognition with tunable filter bank based feature

Barpanda, Soubhagya Sankar; Kumar, Pankaj; Marques, Oge; Majhi, Banshidhar; Bakshi, Sambit

doi:10.1007/s11042-017-4668-z

Cited by 36 publications

(12 citation statements)

References 75 publications

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“…It is believed that points often lie between two data classes in such a way that there is always some margin in between them. SVM maximizes this margin by considering it as a quadratic problem (Barpanda et al, 2018). The SVM can make two possible decisions during iris recognition; acceptance or rejection of a person.…”

Section: Support Vector Machinementioning

confidence: 99%

A fast iris recognition system through optimum feature extraction

Rana

Azam

Akhtar

et al. 2019

PeerJ Computer Science

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With an increasing demand for stringent security systems, automated identification of individuals based on biometric methods has been a major focus of research and development over the last decade. Biometric recognition analyses unique physiological traits or behavioral characteristics, such as an iris, face, retina, voice, fingerprint, hand geometry, keystrokes or gait. The iris has a complex and unique structure that remains stable over a person’s lifetime, features that have led to its increasing interest in its use for biometric recognition. In this study, we proposed a technique incorporating Principal Component Analysis (PCA) based on Discrete Wavelet Transformation (DWT) for the extraction of the optimum features of an iris and reducing the runtime needed for iris template classification. The idea of using DWT behind PCA is to reduce the resolution of the iris template. DWT converts an iris image into four frequency sub-bands. One frequency sub-band instead of four has been used for further feature extraction by using PCA. Our experimental evaluation demonstrates the efficient performance of the proposed technique.

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Section: Support Vector Machinementioning

confidence: 99%

A fast iris recognition system through optimum feature extraction

Rana

Azam

Akhtar

et al. 2019

PeerJ Computer Science

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“…e proposed method still has limitations such as implementation in unrestricted environments and real-time applications [73]. us, Barpanda et al [230] proposed using the wavelet derived from the Cohen-Daubechies-Feauveau 9/7 filter bank to extract iris features in three steps, namely, (1) computing the two-level wavelet from the iris template, (2) computing the power spectrum of the wavelet transformed image, and (3) computing the discrete cosine transform to get the spectrum coefficients [230]. Recently, Barpanda et al [231] proposed applying wavelet mel-cepstrum in order to extract the iris feature.…”

Section: Complexitymentioning

confidence: 99%

Iris Recognition Development Techniques: A Comprehensive Review

2021

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Recently, iris recognition techniques have achieved great performance in identification. Among authentication techniques, iris recognition systems have received attention very much due to their rich iris texture which gives robust standards for identifying individuals. Notwithstanding this, there are several challenges in unrestricted recognition environments. In this article, the researchers present the techniques used in different phases of the recognition system of the iris image. The researchers also reviewed the methods associated with each phase. The recognition system is divided into seven phases, namely, the acquisition phase in which the iris images are acquired, the preprocessing phase in which the quality of the iris image is improved, the segmentation phase in which the iris region is separated from the background of the image, the normalization phase in which the segmented iris region is shaped into a rectangle, the feature extraction phase in which the features of the iris region are extracted, the feature selection phase in which the unique features of the iris are selected using feature selection techniques, and finally the classification phase in which the iris images are classified. This article also explains the two approaches of iris recognition which are the traditional approach and the deep learning approach. In addition, the researchers discuss the advantages and disadvantages of previous techniques as well as the limitations and benefits of both the traditional and deep learning approaches of iris recognition. This study can be considered as an initial step towards a large-scale study about iris recognition.

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“…His algorithms are now the baseline of several strands of research. Many systems have been developed using the iris biometric modality [5,22,36]. Because of their high accuracy and reliability, many researchers have been extensively investigated to use the iris modality in early multi biometric research.…”

Section: Related Workmentioning

confidence: 99%

Multimodal biometric system for ECG, ear and iris recognition based on local descriptors

Regouid

Touahria

Benouis

et al. 2019

Multimed Tools Appl

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Combination of multiple information extracted from different biometric modalities in multimodal biometric recognition system aims to solve the different drawbacks encountered in a unimodal biometric system. Fusion of many biometrics has proposed such as face, fingerprint, iris…etc. Recently, electrocardiograms (ECG) have been used as a new biometric technology in unimodal and multimodal biometric recognition system. ECG provides inherent the characteristic of liveness of a person, making it hard to spoof compared to other biometric techniques. Ear biometrics present a rich and stable source of information over an acceptable period of human life. Iris biometrics have been embedded with different biometric modalities such as fingerprint, face and palm print, because of their higher accuracy and reliability. In this paper, a new multimodal biometric system based ECG-eariris biometrics at feature level is proposed. Preprocessing techniques including normalization and segmentation are applied to ECG, ear and iris biometrics. Then, Local texture descriptors, namely 1D-LBP (One D-Local Binary Patterns), Shifted-1D-LBP and 1D-MR-LBP (Multi-Resolution) are used to extract the important features from the ECG signal and convert the ear and iris images to a 1D signals. KNN and RBF are used for matching to classify an unknown user into the genuine or impostor. The developed system is validated using the benchmark ID-ECG and USTB1, USTB2 and AMI ear and CASIA v1 iris databases. The experimental results demonstrate that the proposed approach outperforms unimodal biometric system. A Correct Recognition Rate (CRR) of 100% is achieved with an Equal Error Rate (EER) of 0.5%.

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Iris recognition with tunable filter bank based feature

Cited by 36 publications

References 75 publications

A fast iris recognition system through optimum feature extraction

A fast iris recognition system through optimum feature extraction

Iris Recognition Development Techniques: A Comprehensive Review

Multimodal biometric system for ECG, ear and iris recognition based on local descriptors

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