Iris Image Reconstruction from Binary Templates

Galbally, Javier; Savvides, Marios; Venugopalan, Shreyas; Ross, Arun

doi:10.1007/978-1-4471-6784-6_20

Cited by 9 publications

(4 citation statements)

References 49 publications

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“…The de facto standard for iris representation is a 2048-bit binary code called IrisCode [9]. How IrisCode can be turned into iris image is demonstrated in [10]. For fingerprint, the template comprises a list of minutiae represented by type, x-and y-coordinates, and an orientation angle [11].…”

Section: ) Synthesis As Template Inversionmentioning

confidence: 99%

A Survey on Synthetic Biometrics: Fingerprint, Face, Iris and Vascular Patterns

2023

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Section: ) Synthesis As Template Inversionmentioning

confidence: 99%

A Survey on Synthetic Biometrics: Fingerprint, Face, Iris and Vascular Patterns

2023

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“…To improve the performance of feature extraction of the iris texture, Ahmadi et al [219] introduced a technique relying on an intelligent hybrid system for iris recognition. e proposed technique is based on a combination of three techniques which are 2-DGK [218], polynomial filtering (PF) [220,221], and step filtering (SF) to extract features of the iris image. For future studies, focus should be on improving IRS by fusing advanced techniques such as deep learning techniques in order to train the algorithms with other evolutionary-based algorithms to reduce the required storage space and computational complexity.…”

Section: Feature Extraction Using Traditional 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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“…The hand-crafted approaches use various image descriptors to calculate image features, which are used to distinguish between authentic irises and artifacts typically through the use of Support Vector Machine classifiers. Popular techniques used in calculation of PAD-related iris image features are Binarized Statistical Image Features (BSIF) [16], Local Binary Patterns (LBP) [12], Binary Gabor Patterns (BGP) [17], Local Contrast-Phase Descriptor (LCPD) [18], Local Phase Quantization (LPQ) [19], Scale Invariant Descriptor (SID) [20], Scale Invariant Feature Transform (SIFT) and DAISY [21], Locally Uniform Comparison Image Descriptor (LUCID) and CENsus TRansform hISTogram (CENTRIST) [22], Weber Local Descriptor (WLD) [18], Wavelet Packet Transform (WPT) [23] or image quality descriptors proposed by Galbally et al [24]. Instead of "hand-crafting" effective feature extractors, one may also benefit from recently popular data-driven approaches that learn directly from the data how to process and classify iris images to solve the PAD task [21], [25]- [29].…”

Section: Related Workmentioning

confidence: 99%

Ensemble of Multi-View Learning Classifiers for Cross-Domain Iris Presentation Attack Detection

Kuehlkamp

Pinto

Rocha

et al. 2019

IEEE Trans.Inform.Forensic Secur.

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The adoption of large-scale iris recognition systems around the world has brought to light the importance of detecting presentation attack images (textured contact lenses and printouts). This work presents a new approach in iris Presentation Attack Detection (PAD), by exploring combinations of Convolutional Neural Networks (CNNs) and transformed input spaces through binarized statistical image features (BSIF). Our method combines lightweight CNNs to classify multiple BSIF views of the input image. Following explorations on complementary input spaces leading to more discriminative features to detect presentation attacks, we also propose an algorithm to select the best (and most discriminative) predictors for the task at hand. An ensemble of predictors makes use of their expected individual performances to aggregate their results into a final prediction. Results show that this technique improves on the current state of the art in iris PAD, outperforming the winner of LivDet-Iris 2017 competition both for intra-and cross-dataset scenarios, and illustrating the very difficult nature of the cross-dataset scenario.

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Iris Image Reconstruction from Binary Templates

Cited by 9 publications

References 49 publications

A Survey on Synthetic Biometrics: Fingerprint, Face, Iris and Vascular Patterns

A Survey on Synthetic Biometrics: Fingerprint, Face, Iris and Vascular Patterns

Iris Recognition Development Techniques: A Comprehensive Review

Ensemble of Multi-View Learning Classifiers for Cross-Domain Iris Presentation Attack Detection

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