Iris Image Acquisition Research Articles

The analysis of Iris image acquisition and real-time detection system using convolutional neural network

The analysis of Iris image acquisition and real-time detection system using convolutional neural network

Generalized Contact Lens Iris Presentation Attack Detection

The high accuracy of iris recognition for person identification has led to its deployment for a variety of applications ranging from border access to mobile unlocking to digital payment. In addition, the commercial success of mobile devices for iris image acquisition enables the easy acquisition of iris images both in an indoor controlled environment as well as an uncontrolled outdoor environment. At the same time, iris recognition systems can easily be attacked using wearable contact lenses. In the literature, several contact lens detection algorithms are proposed; however, the significant drawback is the generalizability under unseen testing domain images. In this research, a novel 3D contact lens iris presentation attack detection algorithm is developed and extensive experiments are performed. The experiments are performed using multiple challenging iris presentation attack databases including the IIITD and LivDet. For the evaluation, we have utilized the experimental protocols, which reflect in-the-wild settings for 3D contact lens iris presentation attack detection where the images belong to both controlled and adverse imaging conditions. The comparison with several state-of-the-art algorithms establishes the effectiveness of the proposed algorithm.

Cross-spectral iris recognition using phase-based matching and homomorphic filtering

In cross-spectral iris recognition, different spectral bands are used to obtain rich information of the human iris. Previous studies on cross-spectral iris recognition are based primarily on feature-based approaches, which are prone to the changes in parameters in the feature extraction process, such as spatial position and iris image acquisition conditions. These parameters can degrade iris recognition performance. In this paper, we present a phase-based approach for cross-spectral iris recognition using phase-only correlation (POC) and band-limited phase-only correlation (BLPOC). A phase-based iris recognition system recognizes an iris using the phase information contained in the iris image; therefore, its performance is not affected by feature extraction parameters. However, the performance of a phase-based cross-spectral iris recognition is strongly influenced by specular reflection. Different illumination conditions may produce different iris images from the same subject. To overcome this challenge, we integrate a photometric normalization technique –homomorphic filtering– with phase-based cross-spectral iris recognition. The experimental results reveal that the proposed technique achieved an excellent matching performance with an equal error rate of 0.59% and a genuine acceptance rate of 95%.

3D iris model and reader for iris identification

SummaryIn the presence of eyelids and eyelashes movement, pupil dilation, poor lighting, blur due to movement during iris image acquisition, and factors that collectively cause distortion in the iris image, 2D image‐based iris identification techniques become limited and might lead to iris misclassification due to the dependency on the image appearance (texture) on the possibly distorted image. To alleviate this problem, we introduce a new 3D iris model and reader based upon which iris identification is performed. Using a set of at least two 2D images taken from different views, a small set of reliable and corresponding salient fiducial points (corner points taken from crypts, corona, and serpentine rings of iris pattern) in the two images are extracted, from which a set of 3D iris salient points are obtained using triangulation. Corresponding salient points in the 2D images are found using the Random Sampling Consensus (RANSAC) algorithm, which is robust in identifying the inlier points that correspond to each other in different views of the iris. From this small and reliable salient point set, a denser (high‐resolution) set of extra salient feature points is constructed at minimum cost using a loop subdivision method that yields corresponding extra salient points in the two images from which a high‐resolution 3D iris model is obtained. The points associated with the 3D high‐resolution model are tessellated to form a high‐resolution triangular mesh with the appearance of a triangular patch in the image imported onto the iris personalized 3D model rendering it a 3D iris surface with appearance. This 3D model construction method allows for a 3D classification of a test iris to one of many possible irises stored in the database. The classification is based on a geometric 3D point cloud error. For cross‐validation, the geometric‐based classification can be augmented by considering a mean squared error (MSE) based on the appearance of corresponding points on the test and base models to further disambiguate between irises with close geometric error values.

Long range iris recognition: A survey

The term “iris” refers to the highly textured annular portion of the human eye that is externally visible. An iris recognition system exploits the richness of these textural patterns to distinguish individuals. Iris recognition systems are being used in a number of human recognition applications such as access control, national ID schemes, border control, etc. To capture the rich textural information of the iris pattern regardless of the eye color, traditional iris recognition systems utilize near-infrared (NIR) sensors to acquire images of the iris. This, however, restricts the iris image acquisition distance to close quarters (less than 1 m). Over the last several years, there have been numerous attempts to design and implement iris recognition systems that operate at longer standoff distances ranging from 1 m to 60 m. Such long range iris acquisition and recognition systems can provide high user convenience and improved throughput. This paper reviews the state-of-the-art design and implementation of iris-recognition-at-a-distance (IAAD) systems. In this regard, the design of such a system from both the image acquisition (hardware) and image processing (algorithms) perspectives are presented. The major contributions of this paper include: (1) discussing the significance and applications of IAAD systems in the context of human recognition, (2) providing a review of existing IAAD systems, (3) presenting a complete solution to the design problem of an IAAD system, from both hardware and algorithmic perspectives, (4) discussing the use of additional ocular information, along with iris, for improving IAAD accuracy, and (5) discussing the current research challenges and providing recommendations for future research in IAAD.

Proof-of-concept and evaluation of a dual function visible/NIR camera for iris authentication in smartphones

As a robust method of person authentication, iris biometrics is making its way in to consumer devices such as smartphones. Current iris image acquisition devices typically work under controlled environment and constrained acquisition conditions. In this paper, the adaption of iris biometrics for unconstrained, hand-held devices such as smartphones is investigated. A prototype device is presented with full system description. This device is equipped with a single image sensor with both visible and NIR sensing capabilities. The device is analysed in terms of its optical properties and iris imaging capabilities. Preliminary results indicate that there are challenges to achieve a reliable recognition performance from the images captured using this device. Current system acquires images with marginal optical quality and spatial resolution in an unconstrained acquisition scenario for iris recognition. Nevertheless, the analyses presented in this paper indicate a similar camera module with improved optics and sensor could combine iris biometrics with conventional front camera functions such as video call and the capture of selfie images.

The Iris Recognition based on Curvelet Transform and Improved SVM

In order to increase the accurate rates of the iris recognition, this paper proposes iris recognition method based on the combination of second generation curvelet transform and the Support Vector Machine Category correction. The images collected from iris image acquisition system are identified. First, rotation correction of iris and spot removal are carried out for collecting iris images. Then, iris images are through rectangular conversion, filter to extract edge point element from which judge the iris image quality. Furthermore, LoG operator is adopted to extract high-frequency energy on both sides of the iris pupil local area, and special judgment is taken out for pupil blocking by eyelids, eyelashes on basis of the energy spectrum. Finally, the cuckoo search algorithm is designed to optimize iris classifier of SVM parameters, and with the second generation curvelet transform algorithm, to complete iris recognition Results show that iris recognition with the CASIA 1.0 database, which contains 756 images of 108 eyes, is an efficient method for iris recognition with high recognition accuracy of iris image. DOI : http://dx.doi.org/10.11591/telkomnika.v12i4.4289

An Iris Image Acquisition Method Based on Embedded System

Capturing an iris image rapidly is required for iris image acquisition embedded system. For the traditional method, an iris image must firstly pass through the quality evaluation, which is complex, so the acquisition speed is very slow. This paper described an iris acquisition embedded system, and a new capturing method. The system includes an DSP DM642, and some relevant peripherals. When an image is acquired, it is filtered by the capture-control function, if passed then be stored the image, until capturing the assigned amount. Using this method, we can rapidly capture the high quality iris images at about 0.8s, and 83% of the stored images can be used for the next procedure. At the same time, it is convenient for the user to capture.

Design and Implementation of a Low-cost Embedded Iris Recognition System on a Dual-core Processor Platform

Design of a low-cost embedded iris recognition system is described in this paper. Firstly, we develop a simple and effective iris image acquisition unit, which is cheap and easy to use. This is achieved by both of hardware design and image evaluation algorithm development. Secondly, the iris recognition algorithm is introduced, including iris segmentation, image normalization, feature extraction, and code matching. The algorithm implementation architecture is based on an embedded dual-core processor platform, Texas Instruments TMS320DM6446 evaluation module (Davinci), which contains an ARM core and a DSP core in one chip. Thirdly, the evaluation experiments are performed on the established iris database, and the experimental results suggest that the system shows good performance both on accuracy and efficiency.

Nonintrusive iris image acquisition system based on a pan-tilt-zoom camera and light stripe projection

Although iris recognition is one of the most accurate biometric technologies, it has not yet been widely used in practical applications. This is mainly due to user inconvenience during the image acquisition phase. Specifically, users try to adjust their eye position within small capture volume at a close distance from the system. To overcome these problems, we propose a novel iris image acquisition system that pro- vides users with unconstrained environments: a large operating range, enabling movement from standing posture, and capturing good-quality iris images in an acceptable time. The proposed system has the follow- ing three contributions compared with previous works: 1 the capture volume is significantly increased by using a pan-tilt-zoom PTZ camera guided by a light stripe projection, 2 the iris location in the large capture volume is found fast due to 1-D vertical face searching from the user's horizontal position obtained by the light stripe projection, and 3 zoom- ing and focusing on the user's irises at a distance are accurate and fast using the estimated 3-D position of a face by the light stripe projection and the PTZ camera. Experimental results show that the proposed sys- tem can capture good-quality iris images in 2.479 s on average at a distance of 1.5 to 3 m, while allowing a limited amount of movement by the user.

System of the Real-Time Acquisition and Recognition for Iris Images

Iris recognition is to identify a user based on the iris texture information which exists between the white sclera and the black pupil. Iris recognition system has been in the limelight for high-security biometric applications due to the advantages of non-contact characteristics and the highest recognition performance among biometric systems. Conventional iris recognition systems consist of the iris camera and the processing unit, like a PC or an embedded control box. The iris camera captures the user's iris images and transfers them to the processing unit. In the processing unit, the captured images are processed and recognition is performed. For fast recognition, it is very important to capture the user's focused eye image at fast speed. If not, the total recognition time is increased and it makes the user feel much inconvenience. In previous researches and systems, they use the focusing method which has been used for general landscape scenes without considering the characteristics of iris image. So, they take much focusing time sometimes, especially in the case of the user with glasses. To overcome such a problem, we propose a new iris image acquisition method to capture the user's focused eye image at very fast speed. It can be applicable to the users both with and without glasses.

A Real-Time Focusing Algorithm for Iris Recognition Camera

For fast iris recognition, it is very important to capture the user's focused eye image at fast speed. Previous researchers have used the focusing method which has been applied to general landscape scenes without considering the characteristics of the iris image. So, they take much focusing time, especially in the case of the user with glasses. To overcome such problems, we propose a new iris image acquisition method to capture focused eye images at very fast speed based on corneal specular reflection. Experimental results show that the focusing time for both users with and without glasses averages 480 ms, and we conclude that our method can be used for the real-time iris recognition camera.

New automated iris image acquisition method

I propose a new iris image acquisition method based on wide- and narrow-view iris cameras. The narrow-view camera has the functionalities of automatic zooming, focusing, panning, and tilting based on the two-dimensional and three-dimensional eye positions detected from the wide- and narrow-view stereo cameras. By using the wide- and narrow-view iris cameras, I compute the user's gaze position, which is used for aligning the X-Y position of the user's eye, and I use the visible-light illuminator for fake-eye detection.