You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
This paper delivers a new database of iris images collected in visible light using a mobile phone's camera and presents results of experiments involving existing commercial and open-source iris recognition methods, namely: Iri-Core, VeriEye, MIRLIN and OSIRIS. Several important observations are made. First, we manage to show that after simple preprocessing, such images offer good visibility of iris texture even in heavily-pigmented irides. Second, for all four methods, the enrollment stage is not much affected by the fact that different type of data is used as input. This translates to zero or close-to-zero Failure To Enroll, i.e., cases when templates could not be extracted from the samples. Third, we achieved good matching accuracy, with correct genuine match rate exceeding 94.5 for all four methods, while simultaneously being able to maintain zero false match rate in every case. Correct genuine match rate of over 99.5 was achieved using one of the commercial methods, showing that such images can be used with the existing biometric solutions with minimum additional effort required. Finally, the experiments revealed that incorrect image segmentation is the most prevalent cause of recognition accuracy decrease. To our best knowledge, this is the first database of iris images captured using a mobile device, in which image quality exceeds this of a near-infrared illuminated iris images, as defined in ISO IEC 19794-6 and 29794-6 documents. This database will be publicly available to all researchers.

Cited Abstract(s):
[1]: Iris is one of the preferred biometric modalities. Nevertheless, the focus of iris image has to be good enough to achieve good recognition performance. Traditional iris imaging devices in the visible spectrum suffer from limited depth-of-field which results in out-of-focus iris images. The acquisition of iris image is thus repeated until a satisfactory focus is obtained or the image is post-processed to improve the visibility of texture pattern. Bad focused images obtained due to non-optimal focus degrade the identification rate. In this work, we propose a novel scheme to capture high quality iris samples by exploring new sensors based on light-field technology to address the limited depth-of-field exhibited by the conventional iris sensors. The idea stems out from the availability of multiple depth focus images in a single exposure. We propose to use the best-focused iris image from the set of depth images rendered by the Light-field Camera (LFC). We further evaluate the proposed scheme experimentally with a unique and newly acquired iris database simulating the real-life scenario.

[2]: The randomness of iris texture has allowed researchers to develop biometric systems with almost flawless accuracies. However, a common drawback of the majority of existing iris recognition systems is the constrained environment in which the user is enroled and recognized. The iris recognition systems typically require a high quality iris image captured under near infrared illumination. A desirable property of an iris recognition system is to be able to operate on colour images, whilst maintaining a high accuracy. In the present work we propose an iris recognition methodology which is designed to cope with noisy colour iris images. There are two main contributions of this paper: first, we adapt standard iris features proposed in literature for near infrared images by applying a feature selection method on features extracted from various colour channels; second, we introduce a Multiple Classifier System architecture to enhance the recognition accuracy of the biometric system. With a feature size of only 360 real valued components, the proposed iris recognition system performs with a high accuracy on UBIRISv1 dataset, in both identification and verfication scenarios.

[3]: Good biometric performance of iris recognition motivates it to be used for many large scale security and access control applications. Recent works have identified visible spectrum iris recognition as a viable option with considerable performance. Key advantages of visible spectrum iris recognition include the possibility of iris imaging in on-the-move and at-a-distance scenarios as compared to fixed range imaging in near-infra-red light. The unconstrained iris imaging captures the images with largely varying radius of iris and pupil. In this work, we propose a new segmentation scheme and adapt it to smartphone based visible iris images for approximating the radius of the iris to achieve robust segmentation. The proposed technique has shown the improved segmentation accuracy up to 85 with standard OSIRIS v4.1. This work also proposes a new feature extraction method based on deepsparsefiltering to obtain robust features for unconstrained iris images. To evaluate the proposed segmentation scheme and feature extraction scheme, we employ a publicly available database and also compose a new iris image database. The newly composed iris image database (VSSIRIS) is acquired using two different smartphones - iPhone 5S and Nokia Lumia 1020 under mixed illumination with unconstrained conditions in visible spectrum. The biometric performance is benchmarked based on the equal error rate (EER) obtained from various state-of-art schemes and proposed feature extraction scheme. An impressive EER of 1.62 is obtained on our VSSIRIS database and an average gain of around 2 in EER is obtained on the public database as compared to the well-known state-of-art schemes.
Raja investigate visible light iris recognition using a light field camera [1] , and also smartphones and tablets [3] , reporting promising results. In the latter scenario, EER (Equal Error Rate) below 2 Segmentation stage is recognized as the most challenging part of the recognition process using noisy images in a study by Radu [2] , where authors manage to achieve 7