Unconstrained visible spectrum iris with textured contact lens variations: Database and benchmarking
TL;DR: The first contact lens database in visible spectrum, Unconstrained Visible Contact Lens Iris (UVCLI) Database, is introduced, containing samples from 70 classes with subjects wearing textured contact lenses in indoor and outdoor environments across multiple sessions and shows that there is a significant scope of improvement in developing efficient PAD algorithms for detection of texturedContact lenses in unconstrained visible spectrum iris images.
Abstract: Iris recognition in visible spectrum has developed into an active area of research This has elevated the importance of efficient presentation attack detection algorithms, particularly in security based critical applications In this paper, we present the first detailed analysis of the effect of textured contact lenses on iris recognition in visible spectrum We introduce the first contact lens database in visible spectrum, Unconstrained Visible Contact Lens Iris (UVCLI) Database, containing samples from 70 classes with subjects wearing textured contact lenses in indoor and outdoor environments across multiple sessions We observe that textured contact lenses degrade the visible spectrum iris recognition performance by over 25% and thus, may be utilized intentionally or unintentionally to attack existing iris recognition systems Next, three iris presentation attack detection (PAD) algorithms are evaluated on the proposed database and highest PAD accuracy of 8285%c is observed This illustrates that there is a significant scope of improvement in developing efficient PAD algorithms for detection of textured contact lenses in unconstrained visible spectrum iris images
Citations
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01 Jan 2023
TL;DR: Wang et al. as discussed by the authors proposed an attention-based deep pixel-wise binary supervision (A-PBS) method for iris PAD, which utilizes pixelwise supervision to capture the fine-grained pixel/patch-level cues and attention mechanism to guide the network to automatically find regions where most contribute to an accurate PAD decision.
Abstract: Iris Presentation Attack Detection (PAD) is essential to secure iris recognition systems. Recent iris PAD solutions achieved good performance by leveraging deep learning techniques. However, most results were reported under intra-database scenarios, and it is unclear if such solutions can generalize well across databases and capture spectra. These PAD methods run the risk of overfitting because of the binary label supervision during the network training, which serves global information learning but weakens the capture of local discriminative features. This chapter presents a novel attention-based deep pixel-wise binary supervision (A-PBS) method. A-PBS utilizes pixel-wise supervision to capture the fine-grained pixel/patch-level cues and attention mechanism to guide the network to automatically find regions where most contribute to an accurate PAD decision. Extensive experiments are performed on six NIR and one visible-light iris databases to show the effectiveness and robustness of proposed A-PBS methods. We additionally conduct extensive experiments under intra-/cross-database and intra-/cross-spectrum for detailed analysis. The results of our experiments indicate the generalizability of the A-PBS iris PAD approach.
TL;DR: This chapter presents a novel attention-based deep pixel-wise binary supervision (A-PBS) method that generalizes well across databases and capture spectra in iris PAD systems.
Abstract: Iris Presentation Attack Detection (PAD) is essential to secure iris recognition systems. Recent iris PAD solutions achieved good performance by leveraging deep learning techniques. However, most results were reported under intra-database scenarios and it is unclear if such solutions can generalize well across databases and capture spectra. These PAD methods run the risk of overfitting because of the binary label supervision during the network training, which serves global information learning but weakens the capture of local discriminative features. This chapter presents a novel attention-based deep pixel-wise binary supervision (A-PBS) method. A-PBS utilizes pixel-wise supervision to capture the fine-grained pixel/patch-level cues and attention mechanism to guide the network to automatically find regions where most contribute to an accurate PAD decision. Extensive experiments are performed on six NIR and one visible-light iris databases to show the effectiveness and robustness of proposed A-PBS methods. We additionally conduct extensive experiments under intra-/cross-database and intra-/cross-spectrum for detailed analysis. The results of our experiments indicates the generalizability of the A-PBS iris PAD approach.
References
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01 Jan 2003
493 citations
"Unconstrained visible spectrum iris..." refers methods in this paper
...contact lens and environmental variations, feature extraction and matching are performed using the classical iris recognition approach by Masek and Kovesi [9] after segmenting the iris region....
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...As the goal of this experiment is to compute baseline iris recognition performance due to the presence of textured contact lens and environmental variations, feature extraction and matching are performed using the classical iris recognition approach by Masek and Kovesi [9] after segmenting the iris region....
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TL;DR: The main purpose of this paper is to announce the availability of the UBIRIS.v2 database, a multisession iris images database which singularly contains data captured in the visible wavelength, at-a-distance and on on-the-move.
Abstract: The iris is regarded as one of the most useful traits for biometric recognition and the dissemination of nationwide iris-based recognition systems is imminent. However, currently deployed systems rely on heavy imaging constraints to capture near infrared images with enough quality. Also, all of the publicly available iris image databases contain data correspondent to such imaging constraints and therefore are exclusively suitable to evaluate methods thought to operate on these type of environments. The main purpose of this paper is to announce the availability of the UBIRIS.v2 database, a multisession iris images database which singularly contains data captured in the visible wavelength, at-a-distance (between four and eight meters) and on on-the-move. This database is freely available for researchers concerned about visible wavelength iris recognition and will be useful in accessing the feasibility and specifying the constraints of this type of biometric recognition.
482 citations
"Unconstrained visible spectrum iris..." refers background in this paper
...v2 [12] as compared to other approaches....
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...v2 [12], MICHE [8], mobile phonebased [19], and VSSIRIS [15]....
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TL;DR: This work presents a segmentation method that can handle degraded images acquired in less constrained conditions, and offers the following contributions: to consider the sclera the most easily distinguishable part of the eye in degraded images, and to run the entire procedure in deterministically linear time in respect to the size of the image.
Abstract: Iris recognition imaging constraints are receiving increasing attention. There are several proposals to develop systems that operate in the visible wavelength and in less constrained environments. These imaging conditions engender acquired noisy artifacts that lead to severely degraded images, making iris segmentation a major issue. Having observed that existing iris segmentation methods tend to fail in these challenging conditions, we present a segmentation method that can handle degraded images acquired in less constrained conditions. We offer the following contributions: 1) to consider the sclera the most easily distinguishable part of the eye in degraded images, 2) to propose a new type of feature that measures the proportion of sclera in each direction and is fundamental in segmenting the iris, and 3) to run the entire procedure in deterministically linear time in respect to the size of the image, making the procedure suitable for real-time applications.
231 citations
"Unconstrained visible spectrum iris..." refers background in this paper
...Research in visible spectrum iris recognition [4, 10, 11, 18] has witnessed significant growth in recent years and is being actively explored....
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TL;DR: A new dataset of iris images acquired by mobile devices can support researchers with regard to biometric dimensions of interest including uncontrolled settings, demographics, interoperability, and real-world applications.
Abstract: A new dataset of iris images acquired by mobile devices can support researchers.MICHE-I will assist with developing continuous authentication to counter spoofing.The dataset includes images from different mobile devices, sessions and conditions. We introduce and describe here MICHE-I, a new iris biometric dataset captured under uncontrolled settings using mobile devices. The key features of the MICHE-I dataset are a wide and diverse population of subjects, the use of different mobile devices for iris acquisition, realistic simulation of the acquisition process (including noise), several data capture sessions separated in time, and image annotation using metadata. The aim of MICHE-I dataset is to make up the starting core of a wider dataset that we plan to collect, with the further aim to address interoperability, both in the sense of matching samples acquired with different devices and of assessing the robustness of algorithms to the use of devices with different characteristics. We discuss throughout the merits of MICHE-I with regard to biometric dimensions of interest including uncontrolled settings, demographics, interoperability, and real-world applications. We also consider the potential for MICHE-I to assist with developing continuous authentication aimed to counter adversarial spoofing and impersonation, when the bar for uncontrolled settings raises even higher for proper and effective defensive measures.
185 citations
"Unconstrained visible spectrum iris..." refers background in this paper
...Various visible spectrum iris databases exist in the literature such as UBIRIS.v2 [12], MICHE [8], mobile phonebased [19], and VSSIRIS [15]....
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...v2 [12], MICHE [8], mobile phonebased [19], and VSSIRIS [15]....
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TL;DR: A new segmentation scheme is proposed and adapted to smartphone based visible iris images for approximating the radius of the iris to achieve robust segmentation and a new feature extraction method based on deepsparsefiltering is proposed to obtain robust features for unconstrained iris image images.
Abstract: 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.
175 citations
"Unconstrained visible spectrum iris..." refers background in this paper
...Various visible spectrum iris databases exist in the literature such as UBIRIS.v2 [12], MICHE [8], mobile phonebased [19], and VSSIRIS [15]....
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...Visible spectrum iris recognition has also gained popularity due to its potential application in the field of mobile biometrics [15, 19]....
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...v2 [12], MICHE [8], mobile phonebased [19], and VSSIRIS [15]....
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