A survey of iris datasets
TL;DR: An overview of the existing publicly available datasets and their popularity in the research community using a bibliometric approach is provided to help investigators conducting research in the domain of iris recognition to identify relevant datasets.
Abstract: Research on human eye image processing and iris recognition has grown steadily over the last few decades. It is important for researchers interested in this discipline to know the relevant datasets in this area to (i) be able to compare their results and (ii) speed up their research using existing datasets rather than creating custom datasets. In this paper, we provide a comprehensive overview of the existing publicly available datasets and their popularity in the research community using a bibliometric approach. We reviewed 158 different iris datasets referenced from the 689 most relevant research articles indexed by the Web of Science online library. We categorized the datasets and described the properties important for performing relevant research. We provide an overview of the databases per category to help investigators conducting research in the domain of iris recognition to identify relevant datasets.
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TL;DR: The researchers present the techniques used in different phases of the recognition system of the iris image and explains the two approaches of iris recognition which are the traditional approach and the deep learning approach.
Abstract: 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.
18 citations
Additional excerpts
...[55] UTIRIS 79 1,540 NIR, VIS NIR (1000× 776) VIS (2048×1360) ....
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TL;DR: A survey of the databases created for ocular recognition, detailing their protocols and how their images were acquired and some relevant works applying deep learning techniques to ocular Recognition point out new challenges and future directions.
Abstract: The use of the iris and periocular region as biometric traits has been extensively investigated, mainly due to the singularity of the iris features and the use of the periocular region when the image resolution is not sufficient to extract iris information. In addition to providing information about an individual’s identity, features extracted from these traits can also be explored to obtain other information such as the individual’s gender, the influence of drug use, the use of contact lenses, spoofing, among others. This work presents a survey of the databases created for ocular recognition, detailing their protocols and how their images were acquired. We also describe and discuss the most popular ocular recognition competitions (contests), highlighting the submitted algorithms that achieved the best results using only iris trait and also fusing iris and periocular region information. Finally, we describe some relevant works applying deep learning techniques to ocular recognition and point out new challenges and future directions. Considering that there are a large number of ocular databases, and each one is usually designed for a specific problem, we believe this survey can provide a broad overview of the challenges in ocular biometrics.
16 citations
06 Oct 2021
TL;DR: In this article, an exhaustive survey of papers on the biometric ILD was performed by searching the most applicable digital libraries, where papers were filtered based on the predefined inclusion and exclusion criteria.
Abstract: Biometrics is progressively becoming vital due to vulnerabilities of traditional security systems leading to frequent security breaches. Biometrics is an automated device that studies human beings’ physiological and behavioral features for their unique classification. Iris-based authentication offers stronger, unique, and contactless identification of the user. Iris liveness detection (ILD) confronts challenges such as spoofing attacks with contact lenses, replayed video, and print attacks, etc. Many researchers focus on ILD to guard the biometric system from attack. Hence, it is vital to study the prevailing research explicitly associated with the ILD to address how developing technologies can offer resolutions to lessen the evolving threats. An exhaustive survey of papers on the biometric ILD was performed by searching the most applicable digital libraries. Papers were filtered based on the predefined inclusion and exclusion criteria. Thematic analysis was performed for scrutinizing the data extracted from the selected papers. The exhaustive review now outlines the different feature extraction techniques, classifiers, datasets and presents their critical evaluation. Importantly, the study also discusses the projects, research works for detecting the iris spoofing attacks. The work then realizes in the discovery of the research gaps and challenges in the field of ILD. Many works were restricted to handcrafted methods of feature extraction, which are confronted with bigger feature sizes. The study discloses that dep learning based automated ILD techniques shows higher potential than machine learning techniques. Acquiring an ILD dataset that addresses all the common Iris spoofing attacks is also a need of the time. The survey, thus, opens practical challenges in the field of ILD from data collection to liveness detection and encourage future research.
7 citations
TL;DR: The authors examined whether next-token prediction succeeds from sentence completion into the realm of actual numerical understanding and highlighted cases for descriptive statistics on in-memory datasets that the LLM initially loads from memory or generates randomly using python libraries.
Abstract: Large language models (LLM) such as OpenAI's ChatGPT and GPT-3 offer unique testbeds for exploring the translation challenges of turning literacy into numeracy. Previous publicly-available transformer models from eighteen months prior and 1000 times smaller failed to provide basic arithmetic. The statistical analysis of four complex datasets described here combines arithmetic manipulations that cannot be memorized or encoded by simple rules. The work examines whether next-token prediction succeeds from sentence completion into the realm of actual numerical understanding. For example, the work highlights cases for descriptive statistics on in-memory datasets that the LLM initially loads from memory or generates randomly using python libraries. The resulting exploratory data analysis showcases the model's capabilities to group by or pivot categorical sums, infer feature importance, derive correlations, and predict unseen test cases using linear regression. To extend the model's testable range, the research deletes and appends random rows such that recall alone cannot explain emergent numeracy.
5 citations
TL;DR: A comprehensive analysis ofDeep learning techniques developed for two main sub-tasks in iris biometrics: segmentation and recognition and delve deep into deep learning techniques for forensic application, especially in post-mortem iris recognition.
Abstract: Inthissurvey, we provide a comprehensive review of more than 200 papers, technical reports, and GitHub repositories published over the last 10 years on the recent developments of deep learning techniques for iris recognition, covering broad topics on algorithm designs, open-source tools, open challenges, and emerging research. First, we conduct a comprehensive analysis of deep learning techniques developed for two main sub-tasks in iris biometrics: segmentation and recognition. Second, we focus on deep learning techniques for the robustness of iris recognition systems against presentation attacks and via human-machine pairing. Third, we delve deep into deep learning techniques for forensic application, especially in post-mortem iris recognition. Fourth, we review open-source resources and tools in deep learning techniques for iris recognition. Finally, we highlight the technical challenges, emerging research trends, and outlook for the future of deep learning in iris recognition. very large VGGFace2 dataset, and fine-tuned for iris recognition using triplet loss. The paper is oriented towards postmortem iris analysis, so the methods use a mixture of live and postmortem images for training and evaluation.
4 citations
References
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TL;DR: A brief overview of the field of biometrics is given and some of its advantages, disadvantages, strengths, limitations, and related privacy concerns are summarized.
Abstract: A wide variety of systems requires reliable personal recognition schemes to either confirm or determine the identity of an individual requesting their services. The purpose of such schemes is to ensure that the rendered services are accessed only by a legitimate user and no one else. Examples of such applications include secure access to buildings, computer systems, laptops, cellular phones, and ATMs. In the absence of robust personal recognition schemes, these systems are vulnerable to the wiles of an impostor. Biometric recognition, or, simply, biometrics, refers to the automatic recognition of individuals based on their physiological and/or behavioral characteristics. By using biometrics, it is possible to confirm or establish an individual's identity based on "who she is", rather than by "what she possesses" (e.g., an ID card) or "what she remembers" (e.g., a password). We give a brief overview of the field of biometrics and summarize some of its advantages, disadvantages, strengths, limitations, and related privacy concerns.
4,678 citations
"A survey of iris datasets" refers background in this paper
...Using multiple biometric characteristics simultaneously in a multimodal system can significantly improve the identification accuracy [37]....
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TL;DR: Algorithms developed by the author for recognizing persons by their iris patterns have now been tested in many field and laboratory trials, producing no false matches in several million comparison tests.
Abstract: Algorithms developed by the author for recognizing persons by their iris patterns have now been tested in many field and laboratory trials, producing no false matches in several million comparison tests. The recognition principle is the failure of a test of statistical independence on iris phase structure encoded by multi-scale quadrature wavelets. The combinatorial complexity of this phase information across different persons spans about 249 degrees of freedom and generates a discrimination entropy of about 3.2 b/mm/sup 2/ over the iris, enabling real-time decisions about personal identity with extremely high confidence. The high confidence levels are important because they allow very large databases to be searched exhaustively (one-to-many "identification mode") without making false matches, despite so many chances. Biometrics that lack this property can only survive one-to-one ("verification") or few comparisons. The paper explains the iris recognition algorithms and presents results of 9.1 million comparisons among eye images from trials in Britain, the USA, Japan, and Korea.
2,829 citations
TL;DR: The statistical variability that is the basis of iris recognition is analysed in this paper using new large databases, presenting the results of 9.1 million comparisons among several thousand eye images acquired in trials in Britain, the USA, Japan and Korea.
Abstract: The statistical variability that is the basis of iris recognition is analysed in this paper using new large databases. The principle underlying the recognition algorithm is the failure of a test of statistical independence on iris phase structure encoded by multi-scale quadrature wavelets. Combinatorial complexity of this phase information across different persons spans about 249 degrees-of-freedom and generates a discrimination entropy of about 3.2 bits/mm 2 over the iris, enabling real-time identification decisions with great enough accuracy to support exhaustive searches through very large databases. This paper presents the results of 9.1 million comparisons among several thousand eye images acquired in trials in Britain, the USA, Japan and Korea.
989 citations
"A survey of iris datasets" refers methods in this paper
...One of the first consistently reported results was obtained by John Daugman when using the UAE database (publicly unavailable)....
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..., near-infrared imaging) andmeet the requirements of the widespread and de facto standard recognition method introduced by John Daugman [1]....
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...Most of the available datasets share a substantial number of properties (e.g., near-infrared imaging) andmeet the requirements of the widespread and de facto standard recognition method introduced by John Daugman [1]....
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TL;DR: This survey covers the historical development and current state of the art in image understanding for iris biometrics and suggests a short list of recommended readings for someone new to the field to quickly grasp the big picture of irisBiometrics.
Abstract: This survey covers the historical development and current state of the art in image understanding for iris biometrics. Most research publications can be categorized as making their primary contribution to one of the four major modules in iris biometrics: image acquisition, iris segmentation, texture analysis and matching of texture representations. Other important research includes experimental evaluations, image databases, applications and systems, and medical conditions that may affect the iris. We also suggest a short list of recommended readings for someone new to the field to quickly grasp the big picture of iris biometrics.
933 citations
"A survey of iris datasets" refers result in this paper
...[6] selected and described 10 datasets....
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...Numerous improvements have been proposed, however, evenwith this well-defined baseline the results have varied tremendously; a result of the implementation methods, datasets, and evaluation protocols used [6]....
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TL;DR: It is shown that LC activation reliably anticipates changes in pupil diameter that either fluctuate naturally or are driven by external events during near fixation, as in many psychophysical tasks.
Abstract: Changes in pupil diameter that reflect effort and other cognitive factors are often interpreted in terms of the activity of norepinephrine-containing neurons in the brainstem nucleus locus coeruleus (LC), but there is little direct evidence for such a relationship. Here, we show that LC activation reliably anticipates changes in pupil diameter that either fluctuate naturally or are driven by external events during near fixation, as in many psychophysical tasks. This relationship occurs on as fine a temporal and spatial scale as single spikes from single units. However, this relationship is not specific to the LC. Similar relationships, albeit with delayed timing and different reliabilities across sites, are evident in the inferior and superior colliculus and anterior and posterior cingulate cortex. Because these regions are interconnected with the LC, the results suggest that non-luminance-mediated changes in pupil diameter might reflect LC-mediated coordination of neuronal activity throughout some parts of the brain.
876 citations
"A survey of iris datasets" refers background in this paper
...In addition, pupil tracking (capturing changes in the size of the pupil over a short time span, typically a few seconds), has been shown to be an important medical biomarker reflecting levels of neurotransmitter and neuronal activity [151]....
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