An improved Daugman method for iris recognition

The popularity of the iris biometric has grown considerably over the past two to three years. Most research has been focused on the development of new iris processing and recognition algorithms for frontal view iris images. However, a few challenging directions in iris research have been identified, including processing of a nonideal iris and iris at a distance. In this paper, we describe two nonideal iris recognition systems and analyze their performance. The word "nonideal" is used in the sense of compensating for off-angle occluded iris images. The system is designed to process nonideal iris images in two steps: 1) compensation for off-angle gaze direction and 2) processing and encoding of the rotated iris image. Two approaches are presented to account for angular variations in the iris images. In the first approach, we use Daugman's integrodifferential operator as an objective function to estimate the gaze direction. After the angle is estimated, the off-angle iris image undergoes geometric transformations involving the estimated angle and is further processed as if it were a frontal view image. The encoding technique developed for a frontal image is based on the application of the global independent component analysis. The second approach uses an angular deformation calibration model. The angular deformations are modeled, and calibration parameters are calculated. The proposed method consists of a closed-form solution, followed by an iterative optimization procedure. The images are projected on the plane closest to the base calibrated plane. Biorthogonal wavelets are used for encoding to perform iris recognition. We use a special dataset of the off-angle iris images to quantify the performance of the designed systems. A series of receiver operating characteristics demonstrate various effects on the performance of the nonideal-iris-based recognition system.

The human iris is traditionally imaged in Near Infrared (NIR) wavelengths (700nm-900nm) for iris recognition. The absorption co-efficient of color inducing pigment in iris, called Melanin, decreases after 700nm thus minimizing its effect when iris is imaged at wavelengths greater than 700nm. This thesis provides an overview and explores the efficacy of iris recognition at different wavelength bands ranging from visible spectrum (450nm-700nm) to NIR (700nm-900nm) and Short Wave Infrared (900nm-1600nm). Different matching methods are investigated at different wavelength bands to facilitate cross-spectral iris recognition.;The iris recognition analysis in visible wavelengths provides a baseline performance when iris is captured using common digital cameras. A novel blob-based matching algorithm is proposed to match RGB (visible spectrum) iris images. This technique generates a match score based on the similarity between blob like structures in the iris images. The matching performance of the blob based matching method is compared against that of classical 'Iris Code' matching method, SIFT-based matching method and simple correlation matching, and results indicate that the blob-based matching method performs reasonably well. Additional experiments on the datasets show that the iris images can be matched with higher confidence for light colored irides than dark colored irides in the visible spectrum.;As part of the analysis in the NIR spectrum, iris images captured in visible spectrum are matched against those captured in the NIR spectrum. Experimental results on the WVU multispectral dataset show promise in achieving a good recognition performance when the images are captured using the same sensor under the same illumination conditions and at the same resolution. A new proprietary 'FaceIris' dataset is used to investigate the ability to match iris images from a high resolution face image in visible spectrum against an iris image acquired in NIR spectrum. Matching in 'FaceIris' dataset presents a scenario where the two images to be matched are obtained by different sensors at different wavelengths, at different ambient illumination and at different resolution. Cross-spectral matching on the 'FaceIris' dataset presented a challenge to achieve good performance. Also, the effect of the choice of the radial and angular parameters of the normalized iris image on matching performance is presented. The experiments on WVU multispectral dataset resulted in good separation between genuine and impostor score distributions for cross-spectral matching which indicates that iris images in obtained in visible spectrum can be successfully matched against NIR iris images using 'IrisCode' method.;Iris is also analyzed in the Short

Face and iris are very common individual bio-metric features for person identification. Face recognition is the method of identification a person uniquely using face. Principal component analysis is one of the algorithms for face recognition. Iris recognition in another method of person identification using iris. Very popular iris recognition method is Daugman algorithm. Unimodal biometric system has various difficulties to detect a person like noisy and unusual data. Multimodal biometric system combined more than one individual modalities like face and iris to increase the efficiency. In this work, we combined principal component analysis and Daugman algorithm with ORL, YALE, CASIA and Real face dataset to combine face and iris recognition to improve the recognition efficiency.

Biometric is an automated recognition process that measures the unique human characteristics, such as iris recognition. Of late, different kinds of iris recognition techniques are available that can be used in different platforms. Daugman algorithm is one of the iris recognition techniques that provide high percentage of accuracy. This study aims to develop a device that performs Iris Recognition using Daugman algorithm on Raspberry Pi. Through the implementation of Daugman algorithm on Raspberry Pi, more applications may come up for iris recognition. Also to make the system adapt with the limitations of Raspberry Pi and the existing application, this work created an image processing function that preprocesses the image before it is passed to the application. Based on the tests implemented, the False Acceptance Rate (FAR) and False Rejection Rate (FRR), the system is mostly to reject registered actors than to accept impostors due to the hardware limitations.

In order to improve the performance of iris recognition, a novel method for iris recognition based on block theory and self-adaptive feature selection is proposed in this paper. Firstly, the normalized iris image is decomposed by convolving with multi-scale and multi-orientation Gabor filters, and then separated into several blocks, the block feature vector which includes mean and variance of Gabor coefficients inside each block can be obtained through statistical techniques, the iris feature vector of the whole iris image is then constructed by conjugating the block feature vector in row column order, finally the two-classifier of iris image are established based on the most distinguishable features, and the multi-classifiers of iris image are established by voting mechanism, and the performance is test by CASIA iris database. The results show that, compared with the traditional iris recognition methods, the proposed method has improved the iris recognition rate.

In this thesis we propose a set of algorithms to reduce the false rejection rate of iris recognition. Even though high recognition accuracy is claimed for iris recognition algorithms, high false rejection rates cause the impediment in worldwide use of iris biometrics.;A novel iris segmentation algorithm for non-ideal iris images treating iris as an elliptical object is proposed. Further, quality of the extracted iris image is improved using SVM based enhancement algorithm. In this algorithm, selected enhancement algorithms globally enhance the iris image and the learning algorithm synergistically fuses local information from these intermediate enhanced images. 1D log polar Gabor wavelet is then used to extract the textural features from the enhanced iris image and Euler numbers are used to extract the topological features. The extracted textural features give a global description of the iris image whereas the topological features are rotation, translation and scaling invariant. These two features are fused using the proposed match score and decision fusion algorithms. Among the three proposed fusion algorithm, SVM learning based match score fusion algorithm outperforms other fusion algorithms. Using CASIA, Miles, UBIRIS and UPOL iris databases, experimental results show that the proposed algorithm gives reduced failure to enroll rate with comparable accuracy.

The use of iris as biometric trait has emerged as one of the most preferred method because of its uniqueness, lifetime stability and regular shape. Moreover it shows public acceptance and new user-friendly capture devices are developed and used in a broadened range of applications. Currently, iris recognition systems work well with frontal iris images from cooperative users. Nonideal iris images are still a challenge for iris recognition and can significantly affect the accuracy of iris recognition systems. In this paper, we propose a method to correct off-angle iris image. Taking into account the eye morphology and the reflectance properties of the external transparent layers, we can evaluate the distorting effect that is present in the acquired image. The correction algorithm proposed includes a first modeling phase of the human eye, a segmentation of the acquired image, and a simulation phase where the acquisition geometry is reproduced and the distortions are evaluated. Finally we obtain an image which does not contain the distorting effects due to jumps in the refractive index. We show how this correction process reduce the intra-class variations for off-angle iris images.

Iris recognition method is the most significant biometric modality for human identification. Currently, multiple deep structured architectures have been employed in biometric recognition with various features like robust generalization ability, high accuracy, and automatic learning. It is also considered as a more emerging area in biometrics along with machine learning approaches. Deep CNN is a conventional approach of image processing broadly utilized in several fields, however, it is simply influenced by slight disturbances and also includes bad anti-noise capacity in image classification. The major scope of this work is to implement adaptive deep learning-aided iris recognition. The key contribution of this paper relies on enhancing iris segmentation and iris recognition. Initially, the iris images are pre-processed, which are subjected to segmentation. The segmentation approach focuses on a Hough transform with K-Means Clustering (HT-KMC). Once the segmentation of the iris is done, the “Enhanced Faster Region-Convolutional Neural Network (E-Faster-RCNN)” is developed for the recognition purpose. The number of suitably hidden neurons of the conventional Faster-RCNN is optimized by the Adaptive-Sail Fish Optimization (ASFO) algorithm to obtain the highest efficiency. The simulation findings of the recommended approach proved that the method is effective in iris recognition by experimenting on a benchmark dataset.

The performance of iris recognition systems is significantly affected by the segmentation accuracy, especially in non-ideal iris images. This paper proposes an improved method to localise non-circular iris images quickly and accurately. Shrinking and expanding active contour methods are consolidated when localising inner and outer iris boundaries. First, the pupil region is roughly estimated based on histogram thresholding and morphological operations. Thereafter, a shrinking active contour model is used to precisely locate the inner iris boundary. Finally, the estimated inner iris boundary is used as an initial contour for an expanding active contour scheme to find the outer iris boundary. The proposed scheme is robust in finding exact the iris boundaries of non-circular and off-angle irises. In addition, occlusions of the iris images from eyelids and eyelashes are automatically excluded from the detected iris region. Experimental results on CASIA v3.0 iris databases indicate the accuracy of proposed technique.

Iris Recognition (IR) across illumination variations is a problem of fundamental importance in computer vision. In this paper, we propose two novel techniques, viz., Triangular DCT based Feature Extraction (T-DCT) and Radon Transform based Pre-processing, to improve the performance of an IR system. T-DCT is used for efficient feature extraction and Radon Transform is used for curve detection in iris images. A Binary Particle Swarm Optimization (BPSO) based feature selection algorithm is used to search the feature space for optimal feature subset. Individual stages of the IR system are examined and an attempt is made to improve each stage. Experimental results obtained by applying the proposed algorithm on three benchmark iris databases, namely Phoenix, MMU and IITD dataset, show that the proposed system out performs other IR systems. A significant increase in the recognition rate and a substantial reduction in the number of selected features are observed.

Neural Network Approach to Iris Recognition in Noisy Environment

Objective: Iris recognition is one of the emerging areas as the demand for security in social and personal areas is increasing day by day. The most challenging step in the process of iris recognition is accurate iris localization. Methods/Statistical Analysis: In this paper, we propose an iris recognition method in light of Teaching Learning Based Optimisation (ITLBO) to choose the ideal components subset. The iris information, for the most part, contains a tremendous number of textural elements and a similarly modest number of tests per subject, which make the accurate iris pattern classification challenging. Findings: Feature selection scheme is used to identify the most important and irrelevant features from extracted features set of a relatively high dimension based on some selection criterions. It is not generally handy to gather an extensive number of tests because of some security issues. In this paper, we propose ITLBO to enhance the feature subset determination by consolidating important results from different component choice strategies. Application/Improvements: The principle target of ITLBO is to accomplish an adjust the Recognition Rate (RR), the False Acceptance Rate (FAR), the False Reject Rate (FRR) and they chose feature subset measure. The proposed method is computationally successful with the RR of 97.97 % on the CASIA iris datasets.

The ability to identify individuals based on their iris is known as iris recognition. Over the past decade iris recognition has garnered much attention because of its strong performance in comparison with other mainstream biometrics such as fingerprint and face recognition. Performance of iris recognition systems is driven by application scenario requirements. Standoff distance, subject cooperation, underlying optics, and illumination are a few examples of these requirements which dictate the nature of images an iris recognition system has to process. Traditional iris recognition systems, dubbed "stop and stare", operate under highly constrained conditions. This ensures that the captured image is of sufficient quality so that the success of subsequent processing stages, segmentation, encoding, and matching are not compromised. When acquisition constraints are relaxed, such as for surveillance or iris on the move, the fidelity of subsequent processing steps lessens.;In this dissertation we propose a multi-faceted framework for mitigating the difficulties associated with non-ideal iris. We develop and investigate a comprehensive iris image quality metric that is predictive of iris matching performance. The metric is composed of photometric measures such as defocus, motion blur, and illumination, but also contains domain specific measures such as occlusion, and gaze angle. These measures are then combined through a fusion rule based on Dempster-Shafer theory. Related to iris segmentation, which is arguably one of the most important tasks in iris recognition, we develop metrics which are used to evaluate the precision of the pupil and iris boundaries. Furthermore, we illustrate three methods which take advantage of the proposed segmentation metrics for rectifying incorrect segmentation boundaries. Finally, we look at the issue of iris image interoperability and demonstrate that techniques from the field of hardware fingerprinting can be utilized to improve iris matching performance when images captured from distinct sensors are involved.

In the health care systems, a high security level is required to protect extremely sensitive patient records. The goal is to provide a secure access to the right records at the right time with high patient privacy. As the most accurate biometric system, the iris recognition can play a significant role in healthcare applications for accurate patient identification. In this paper, the corner stone towards building a fast and robust iris recognition system for healthcare applications is addressed, which is known as iris localization. Iris localization is an essential step for efficient iris recognition systems. The presence of extraneous features such as eyelashes, eyelids, pupil and reflection spots make the correct iris localization challenging. In this paper, an efficient and automatic method is presented for the inner and outer iris boundary localization. The inner pupil boundary is detected after eliminating specular reflections using a combination of thresholding and morphological operations. Then, the outer iris boundary is detected using the modified Circular Hough transform. An efficient preprocessing procedure is proposed to enhance the iris boundary by applying 2D Gaussian filter and Histogram equalization processes. In addition, the pupil's parameters (e.g. radius and center coordinates) are employed to reduce the search time of the Hough transform by discarding the unnecessary edge points within the iris region. Finally, a robust and fast eyelids detection algorithm is developed which employs an anisotropic diffusion filter with Radon transform to fit the upper and lower eyelids boundaries. The performance of the proposed method is tested on two databases: CASIA Version 1.0 and SDUMLA-HMT iris database. The Experimental results demonstrate the efficiency of the proposed method. Moreover, a comparative study with other established methods is also carried out.

Iris recognition has emerged as one of the most accurate and convenient biometric for person identification and has been increasingly employed in a wide range of e-security applications. The quality of iris images acquired at-a-distance or under less constrained imaging environments is known to degrade the iris recognition accuracy. The periocular information is inherently embedded in such iris images and can be exploited to assist in the iris recognition under such non-ideal scenarios. Our analysis of such iris templates also indicates significant degradation and reduction in the region of interest, where the iris recognition can benefit from a similarity distance that can consider importance of different binary bits, instead of the direct use of Hamming distance in the literature. Periocular information can be dynamically reinforced, by incorporating the differences in the effective area of available iris regions, for more accurate iris recognition. This article presents such a periocular-assisted dynamic framework for more accurate less-constrained iris recognition. The effectiveness of this framework is evaluated on three publicly available iris databases using within-dataset and cross-dataset performance evaluation, e.g., improvement in the recognition accuracy of 22.9%, 10.4% and 14.6% on three databases under both the verification and recognition scenarios.