Deep learning architecture for iris recognition based on optimal Gabor filters and deep belief network

He, Fei; Han, Ye; Wang, Han; Ji, Jinchao; Liu, Yuanning; Ma, Zhiqiang

doi:10.1117/1.jei.26.2.023005

Cited by 42 publications

(15 citation statements)

References 55 publications

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“…Accordingly, in the proposed approach, image intensities are normalized using histogram equalization (Ali et al, 2016) and Gaussian un‐sharp mask is applied to enhanced its quality. Finally, a median filter of size 4 × 4 is applied to remove noise (He et al, 2017; Phetchanchai, Selamat, Saba, & Rehman, 2010; Rahim, Norouzi, Rehman, & Saba, 2017; Rahim, Rehman, Kurniawan, & Saba, 2017).…”

Section: Proposed Methodologymentioning

confidence: 99%

Light microscopic iris classification using ensemble multi‐class support vector machine

Rehman

2021

Microscopy Res & Technique

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Similar to other biometric systems such as fingerprint, face, DNA, iris classification could assist law enforcement agencies in identifying humans. Iris classification technology helps law‐enforcement agencies to recognize humans by matching their iris with iris data sets. However, iris classification is challenging in the real environment due to its invertible and complex texture variations in the human iris. Accordingly, this article presents an improved Oriented FAST and Rotated BRIEF with Bag‐of‐Words model to extract distinct and robust features from the iris image, followed by ensemble multi‐class‐SVM to classify iris. The proposed methodology consists of four main steps; first, iris image normalization and enhancement; second, localizing iris region; third, iris feature extraction; finally, iris classification using ensemble multi‐class support vector machine. For preprocessing of input images, histogram equalization, Gaussian mask and median filters are applied. The proposed technique is tested on two benchmark databases, that is, CASIA‐v1 and iris image database, and achieved higher accuracy than other existing techniques reported in state of the art.

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Section: Proposed Methodologymentioning

confidence: 99%

Light microscopic iris classification using ensemble multi‐class support vector machine

Rehman

2021

Microscopy Res & Technique

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“…To develop the performance of iris segmentation of the OSIRISV4 open-source model for these challenges, Yahiaoui et al [89] introduced a method by extending the Viterbi algorithm [140] by adding statistical techniques for iris segmentation based on unsupervised approaches, and they focused especially on the hidden Markov chain method as suggested in [89,141,142]. To increase the performance rate of the segmentation phase of iris images, some limitations were still prevalent such as reflection from glasses and different scanners as well as reflection occluded by eyelids and eyelashes, leading He et al [143] and Liu et al [35] to further develop the Hough transform (HT) method based on the edge detection method which was applied by Canny [144] to segment the iris region.…”

Section: Histogram-and Contour-based Segmentationmentioning

confidence: 99%

“…e Hough transform method was used to detect boundaries by exploiting the duplicity between points on the boundaries and the parameters which were the coordinates of the center and radius of those boundaries [135], and these parameters were determined based on the weight of the matrix after the threshold of the radius was given [35,143].…”

Section: Histogram-and Contour-based Segmentationmentioning

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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“…Further, as a deep CNN model, it doesn't have measures to cope with issues such as weight saturation and vanishing gradient that encounters due to the increased model depth. Later on, authors in [19] employed a deep belief network (DBN) while using an adaptive Gabour filter as a core component. Again, DBN was a straightforward application of the IrisCode without any optimization.…”

Section: Literature Analysis Of Iris Recognitionmentioning

confidence: 99%

CCRNet: a novel data-driven approach to improve cross-domain Iris recognition

Choudhary

Tiwari

Udutalapally

2020

Multimed Tools Appl

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In spite of the prominence and robustness of iris recognition systems, iris images acquisition using heterogeneous cameras/sensors, is the prime concern in deploying them for wide-scale applications. The textural qualities of iris samples (images) captured through distinct sensors substantially differ due to the differences in illumination and the underlying hardware that yields intra-class variation within the iris dataset. This paper examines three miscellaneous configurations of convolution and residual blocks to improve cross-domain iris recognition. Further, the finest architecture amongst three is identified by the Friedman test, where the statistical differences in proposed architectures are identified based on the outcomes of Nemeny and Bonferroni-Dunn tests. The quantitative performances of these architectures are perceived on several experiments simulated on two iris datasets; ND-CrossSensor-Iris-2013 and ND-iris-0405. The finest model is referred to as "Collaborative Convolutional Residual Network (CCRNet)" and is further examined on several experiments prepared in similar and cross-domains. Results depict that least two error rates reported by CCRNet are 1.06% and 1.21% that enhances the benchmark for the state of the arts. This is due to fast convergence and rapid weights updation achieved from convolution and residual connections, respectively. It helps in recognizing the micro-patterns existing within the iris region and results in better feature discrimination among large numbers of iris subjects.

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Deep learning architecture for iris recognition based on optimal Gabor filters and deep belief network

Cited by 42 publications

References 55 publications

Light microscopic iris classification using ensemble multi‐class support vector machine

Light microscopic iris classification using ensemble multi‐class support vector machine

Iris Recognition Development Techniques: A Comprehensive Review

CCRNet: a novel data-driven approach to improve cross-domain Iris recognition

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