Deep belief networks for iris recognition based on contour detection

Baqar, Mohtashim; Ghani, Azfar; Aftab, Azeem; Arbab, Saira; Yasin, Sajid

doi:10.1109/icosst.2016.7838580

Cited by 18 publications

(10 citation statements)

References 11 publications

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“…Euclidean distance between these features are evaluated to calculate the similarity score. Bagar et al [1] proposed deep belief network based iris classification framework. The architecture is primarily inspired from those used for natural images, they are complex and deep which needs enough training data unlike iris datasets which lacks enormous training samples required for effective training of such complex nets.…”

Section: Deep Learning Approachesmentioning

confidence: 99%

Texture Aware Autoencoder Pre-training And Pairwise Learning Refinement For Improved Iris Recognition

Chakraborty¹,

Chakraborty²,

Biswas³

et al. 2022

Preprint

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This paper presents a texture aware end-to-end trainable iris recognition system, specifically designed for datasets like iris having limited training data. We build upon our previous stagewise learning framework with certain key optimization and architectural innovations. First, we pretrain a Stage-1 encoder network with an unsupervised autoencoder learning optimized with an additional data relation loss on top of usual reconstruction loss. The data relation loss enables learning better texture representation which is pivotal for a texture rich dataset such as iris. Robustness of Stage-1 feature representation is further enhanced with an auxiliary denoising task. Such pre-training proves beneficial for effectively training deep networks on data constrained iris datasets. Next, in Stage-2 supervised refinement, we design a pairwise learning architecture for an end-to-end trainable iris recognition system. The pairwise learning includes the task of iris matching inside the training pipeline itself and results in significant improvement in recognition performance compared to usual offline matching. We validate

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Section: Deep Learning Approachesmentioning

confidence: 99%

Texture Aware Autoencoder Pre-training And Pairwise Learning Refinement For Improved Iris Recognition

Chakraborty¹,

Chakraborty²,

Biswas³

et al. 2022

Preprint

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“…Type of noise Proposed method Nixon and Aguado [65] Noise Median filter (MF) Huang et al [66] Blurriness Kernel estimation of the blur Lee et al [67] Reflections Line intensity profile (LIP) Santos and Hoyle [68] Low lighting (low illumination) CLAHE method Dehkordi and Abu-Bakar [69] Noise such as eyelids, eyelash, and light reflections and pupil pixels Multiple thresholding method Liu et al [70] Poor performance accuracy of low-resolution (LR) iris recognition A heterogeneous metric learning algorithm Raffei et al [71] Low contrast Adaptive histogram equalization Bakshi et al [72] Occlusions due to eyelashes and eyelids Gaussian filters and the Hough line detector Kumar et al [73] e difference between brighter and darker pixels Top-hat and bottom-hat filters Baqar et al [74] Specular highlight due to reflection 2D linear interpolation Djoumessi [75] Occlusions due to eyelids Hough line detector Gangwar et al [76] Specular reflection reshold Radman et al [77] Reflections Morphological-retinex method Ribeiro et al [78] Low resolution and quality of iris images Stacked autoencoder (SAE) technique and convolutional neural network (CNN) technique Arsenovic et al [79] Shadows on the eye images Histogram normalization Gad et al [80] Specular reflection Morphological operations Susitha and Subban [81] Low quality due to poor contrast CLAHE method Das and Derakhshani [82] Poor contrast and brightness BPDFHE method Donida et al [83] Specular reflections and noise Inpainting algorithm and Gaussian-based bilateral filter Complexity the iris image becomes more difficult due to different noise types such as the off-angle imaging, blurring, occlusion caused by eyelids and eyelashes, and specular reflection from illumination [104]. Raffei et al [105] proposed a fusion technique which consists of three substages.…”

Section: Sourcementioning

confidence: 99%

“…For the classification of iris pattern, Baqar et al [74] applied a technique which is dependent on a deep belief network (DBN) through restricted Boltzmann machine (RBM) with modified backpropagation algorithm-based feed-forward neural network (RVLR-NN) [74]. He et al introduced a method which used a deep belief network (DBN) to implement the iris classification [143].…”

Section: Classification Using Deep Learning Techniquesmentioning

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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“…In [149], Baqar and colleagues proposed an iris recognition framework based on deep belief networks, as well as contour information of iris images. Contour based feature vector has been used to discriminate samples belonging to different classes i.e., difference of sclerairis and iris-pupil contours, and is named as Unique Signature.…”

Section: Deep Learning Work On Iris Recognitionmentioning

confidence: 99%

Biometrics Recognition Using Deep Learning: A Survey

Minaee¹,

Abdolrashidi²,

Su³

et al. 2019

Preprint

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Deep learning-based models have been very successful in achieving state-of-the-art results in many of the computer vision, speech recognition, and natural language processing tasks in the last few years. These models seem a natural fit for handling the everincreasing scale of biometric recognition problems, from cellphone authentication to airport security systems. Deep learning-based models have increasingly been leveraged to improve the accuracy of different biometric recognition systems in recent years. In this work, we provide a comprehensive survey of more than 120 promising works on biometric recognition (including face, fingerprint, iris, palmprint, ear, voice, signature, and gait recognition), which deploy deep learning models, and show their strengths and potentials in different applications. For each biometric, we first introduce the available datasets that are widely used in the literature and their characteristics. We will then talk about several promising deep learning works developed for that biometric, and show their performance on popular public benchmarks. We will also discuss some of the main challenges while using these models for biometric recognition, and possible future directions to which research in this area is headed.

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Deep belief networks for iris recognition based on contour detection

Cited by 18 publications

References 11 publications

Texture Aware Autoencoder Pre-training And Pairwise Learning Refinement For Improved Iris Recognition

Texture Aware Autoencoder Pre-training And Pairwise Learning Refinement For Improved Iris Recognition

Iris Recognition Development Techniques: A Comprehensive Review

Biometrics Recognition Using Deep Learning: A Survey

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