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Convolutional Neural Networks (CNNs) have shown great success in solving key artificial vision challenges such as image segmentation. Training these networks, however, normally requires plenty of labeled data, while data labeling is an expensive and time-consuming task, due to the significant human effort involved. In this paper we propose two pixel-level domain adaptation methods, introducing a training...
The iris has remained a preferred biometric trait compared to other biometrics because of its uniqueness, stability properties. However, degraded iris images captured under less constrained acquisition setups and varying lighting conditions will affect the performance of iris based biometrics systems. This paper presents a novel method for iris recognition through symbolic modeling approach and also...
Iris recognition is a biometric authentication system proving vital for ensuring security and has been employed as an important case to test the algorithms developed in pattern recognition. The unique circular shape of the iris and its time invariance makes it a versatile technique that has an accuracy that can be mathematically proven. Here in this work we propose a new segmentation technique and...
For decades iris recognition has been widely studied by the scientific community due to its almost unique and stable patterns. Iris recognition biometric systems apply mathematical pattern-recognition techniques to an iris' image of an individual's eye to extract its feature vector. Comparing the dissimilarities from two feature vectors with an acceptance threshold, the system decides if the two vectors...
This paper presents a unique Comparative Study of Features Fusion Techniques for Iris. In 1993, Daughman developed IrisCode and it is influenced many researchers to develop different Iris Recognition System using different techniques. Iris Features are extracted using existing techniques like 2D-FFT, DWC, LBP, PCA etc. More information is extracted by fusing multiple features. Feature Fusion is a...
Pupil localization is the most significant preprocessing step in recognizing the iris. Iris images are often degraded by low resolution, specular reflections; occlusion by eyelids, contact lenses etc. In this paper, a novel approach, which combines smoothing of iris images and segmenting the pupil, is proposed. First, a fractional derivative mask is used for smoothening the iris images, which acts...
Contact lens detection in the eye is a significant task to improve the reliability of iris recognition systems. A contact lens overlays the iris region and prevents the iris sensor from capturing the normal iris region. In this paper, we present a novel scheme for detection to detecting a contact lens using Deep Convolutional Neural Network (CNN). The proposed CNN architecture ContlensNet is structured...
Iris recognition is effective biometric technique that gained attention in past 20 years. Over past few years many techniques and algorithms are proposed for effective iris recognition under various constraints. Low constraint IRIS recognition is still an area where considerable work needs to be done and there is huge scope to do. This paper discusses about various techniques proposed, the constraints...
This study examines the quality of the image in different irises during iris recognition. The main purpose of this study is to provide reliable evidence pertaining to the superiority of the iris to other biometrics particularly in the presence of noise coming with images captured by the iris. The study mainly used the iris database CASIA-IrisV3-interval as input. It also examined and applied iris...
This paper describes iris biometric matching performed using the iris pictures captured by the standard visible spectrum smart phone cameras from the MICHE II database. Our method uses a combination of a popular iris code approach and a periocular biometric based on the Multi-Block Transitional Local Binary Patterns. The authentication scores are calculated separately, and the results are combined...
Person recognition is a challenging research problem particularly if the images are captured at a distance and only ocular region is present. In this research, we present a framework that extracts multiple features from iris and periocular regions from near infrared images captured at a distance of 2 meters or more. Using these features and random decision forest, fusion and classification is performed...
This paper presents a state-of-the-art iris segmentation framework specifically for non-ideal irises. The framework adopts coarse-to-fine strategy to localize different boundaries. In the approach, pupil is coarsely detected using an iterative search method exploiting dynamic thresholding and multiple local cues. The limbic boundary is first approximated in polar space using adaptive filters and then...
This paper gives a survey on Iris Biometric recognition technique for past few years. Iris is one of the most promising, reliable, and robust biometric technology. In this paper, advancements in research methodologies used by different researchers for iris localization, iris segmentation, feature extraction, and classification are discussed. The limitations of existing algorithms and their results...
Iris recognition is the most reliable and accurate biometric identification system. Iris recognition system captures an image of an individual's eye, the iris in the image is segmented and normalized for extracting its feature. The performance of iris recognition systems depends on the process of segmentation of iris form the eye image. Segmentation is the most important part in iris recognition process...
Periocular biometrics has been established as an independent modality due to concerns on the performance of iris or face systems in uncontrolled conditions. Periocular refers to the facial region in the eye vicinity, including eyelids, lashes and eyebrows. It is available over a wide range of acquisition distances, representing a trade-off between the whole face (which can be occluded at close distances)...
Iris recognition system is the secure authentication tool to control access to physical assets based on iris texture. It is the most reliable biometric system because of its uniqueness and stability over time, even the genetically identical twins have different Iris textures. This paper presents a system that is immune to tilt and scale variations. The iris region of interest is segmented based on...
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...
Biometric systems are developed to verily and-or identify persons, for access control, identification systems or border management. They are based on the unique biological characteristics of persons like face, finger, iris, hand, signature, voice. In comparing with other biometric technologies, the iris recognition system has very high recognition accuracy. This system consists of several stages including...
Previous work on iris recognition focused on either Visible Light (VL), Near-Infrared (NIR) imaging or the fusion between them. However, limited numbers of works have compared the iris biometric performance under both VL and NIR spectrum using images taken from the same subject. In this paper, we explore the differences in iris recognition performance across the VL and NIR spectrum. In addition, we...
Currently, identity management systems work with heterogeneous iris images captured by different types of iris sensors. Indeed, iris recognition is being widely used in different environments where the identity of a person is necessary. Therefore, it is a challenging problem to maintain a stable iris recognition system which is effective for all type of iris sensors. This paper proposes a new cross-sensor...
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