Iris recognition based on statistical learning

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.

Human iris is one of the most reliable biometrics because of its uniqueness, stability and noninvasive nature.Thus it has attracted the attention of biometrics based identification and verification research and development community.Iris recognition has been a fast growing, challenging and interesting area in real-time applications.A large number of iris recognition algorithms have been developed for decades.This paper presents a comparative study of the performance from the iris authentication using Discrete Cosine Transform (DCT), Haar wavelet transform and a proposed Column Mean Method based features.Here iris recognition is done using the image feature set extracted from DCT, Haar Wavelet transform and Column Mean.Analysis was performed with the mentioned methods, consisting of the False Acceptance Rate (FAR) and the Genuine Acceptance Rate (GAR).The proposed technique is tested on an iris image database having 384 images.The iris recognition systems that produce very low error rates were successfully designed.DCT and Column Mean Methods give better performance with the accuracy of 98.44% and Haar Wavelet Transform gives a performance with the accuracy of 97.66%.The proposed Column Mean Method is superior in having faster recognition rate.

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.

In this study, a hybrid security system is proposed. The proposed system is composed of two subsystems namely iris recognition system (IRS) and speaker recognition system (SRS). Pre-processing, feature extraction and feature matching are the main steps of these systems. In IRS subsystem, Gaussian filter, Canny edge detector, Hough transform, and histogram equalization is performed for pre-processing, respectively. After that, by applying 4-level Discrete Wavelet Transform (DWT) to pure iris image, the iris image is decomposed into four sub-bands (LL4, LH4, HL4 and HH4). In order to extract the feature vector from iris pattern, the LH4, HL4 and HH4 sub-bands (matrices) are merged into one matrix. Finally the matrix is transformed in vector to obtain the feature vector of iris image. For SRS subsystem, the pre-processing step includes spectral arrangement, silence part removing and band limitation operations. After pre-processing, frame blocking and windowing are applied to the long-term speech samples and then Fast Fourier Transform (FFT) is performed for the each short-term speech segments (frames). Finally, the Mel Frequency Cepstral Coefficients (MFCC) technique is performed in order to obtain feature vector of the speech. The feature matching step of both IRS and SRS is implemented with Dynamic Time Warping (DTW) which is an efficient algorithm to measure the distance between two vectors. According to the DTW results, the false acceptance rate (FAR) is zero and false rejecting rate (FRR) is about 4 % for the proposed hybrid system.

Iris recognition is one of the most powerful techniques for biometric identification. The requirement for current systems is to acquire multiple iris codes from the same eye and evaluate which bits are the most consistent bits in the iris code. When the acquired images are noisy, the inconsistent bits in the iris code should be masked to improve performance. This paper thoroughly investigates the use of multiple training samples for enrolment. Based on this, an enhanced iris recognition approach is proposed employing the fusion of a set of iris images of a given eye using the most consistent feature data. The algorithm reduces the database size and accelerates the matching process. The Chinese Academy of Sciences – Institute of Automation (CASIA) iris database is used to simulate the studies. The approach used in the proposed work outperforms existing approaches with the fact that in the proposed iris recognition system, verification is done with a single template (final fused template) instead of multiple base templates as done by the existing works.

This study focuses on iris recognition using deep learning techniques. The EfficientDet network model was employed for both iris detection and recognition tasks. Four datasets were utilized to train and evaluate the deep learning network. The model was trained to extract iris features and classify individuals based on their unique iris patterns. The proposed method achieved a high recognition rate of over 98% across multiple dataset evaluations. For real‐time implementation on an embedded system, the trained model was quantized to an 8‐bit integer format to accommodate resource‐constrained devices. Despite this quantization, the recognition accuracy remained high, reaching 97%. By incorporating an Edge TPU accelerator alongside a Raspberry Pi system, the processing speed reached up to 10 frames per second during real‐time iris camera testing, demonstrating the feasibility of real‐time iris recognition. An intruder test was conducted to assess the system's robustness in preventing unauthorized access. The False Acceptance Rate (FAR) was measured to assess the likelihood of incorrectly accepting an unauthorized individual. Experimental results show that the FAR can be reduced to zero by applying additional temporal constraints, effectively preventing unauthorized individuals from passing the iris recognition‐based access control system.

<p>In today’s sensitive environment, for personal authentication, iris recognition is the most attentive technique among the various biometric technologies. One of the key steps in the iris recognition system is the accurate iris segmentation from its surrounding noises including pupil and sclera of a captured eye-image. In our proposed method, initially input image is preprocessed by using bilateral filtering. After the preprocessing of images contour based features such as, brightness, color and texture features are extracted. Then entropy is measured based on the extracted contour based features to effectively distinguishing the data in the images. Finally, the convolution neural network (CNN) is used for the effective sclera, iris and pupil parts segmentations based on the entropy measure. The proposed results are analyzed to demonstrate the better performance of the proposed segmentation method than the existing methods.</p>

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.

The iris is a stable biometric trait that has been widely used for human recognition in various applications. However, deployment of iris recognition in forensic applications has not been reported. A primary reason is the lack of human-friendly techniques for iris comparison. To further promote the use of iris recognition in forensics, the similarity between irises should be made visualizable and interpretable. Recently, a human-in-the-loop iris recognition system was developed, based on detecting and matching iris crypts. Building on this framework, we propose a new approach for detecting and matching iris crypts automatically. Our detection method is able to capture iris crypts of various sizes. Our matching scheme is designed to handle potential topological changes in the detection of the same crypt in different images. Our approach outperforms the known visible-feature-based iris recognition method on three different data sets. In particular, our approach achieves over 22% higher rank one hit rate in identification, and over 51% lower equal error rate in verification. In addition, the benefit of our approach on multi-enrollment is experimentally demonstrated.

This paper presents a novel high-security iris recognition system using elliptic curve cryptography. In this system, the original IrisCode is partitioned out into binary-shards then every binary-shard is represented by a point over the elliptic curve and encrypted via elliptic curve cryptography. As a result, the protected IrisCodes can be stored and transmitted from the local iris readers to the centralized server as well as between various servers more securely. The proposed iris recognition system is evaluated using CASIA-IrisV3 database in terms of accuracy, security, and privacy.

Among all biometric technologies, iris recognition is the most accurate and high confidence authentication system. Due to the limitations in PCA-based system, modified principal component analysis (PCA)-based feature extraction is proposed in iris recognition system. In the proposed system, features are extracted using Gaussian kernel PCA (GKPCA) and classified using support vector machine (SVM). GKPCA and SVM algorithms are evaluated using CASIA V3 iris database. The performances are compared with the existing PCA-based system. The proposed system achieves 96.67% of accuracy for 256 features using GKPCA linear SVM. False acceptance rate (FAR) and false rejection rate (FRR) are 0 and 3 respectively for linear SVM. The results show that the proposed system performs accurate localisation of patterns even in non-ideal conditions.

Iris recognition system is one of the most predominant methods used for personal identification in the modern days. Low quality iris image such as low contrast and poor illumination presents a setback for iris recognition as the acceptance or rejection rates of verified user depend solely on the image quality. This paper presents a new method for improving histogram equalization technique to obtained high contrast in normalization process thereby reducing False Rejection Rate (FRR) and False Acceptance Rate (FAR). The proposed technique is developed using C++ and tested using four datasets CASIA, UBIRIS, MMU and ICE 2005. The experimental results show that the proposed technique has an accuracy of 95%, as compared to the existing techniques: CLAHE, AHE, MAHE and HE which have an accuracy of a 93.0, 85.7, 92.8 and 90.71% respectively. Hence it can be concluded that the proposed technique is a better enhancement technique compared to the existing techniques for image enhancement.

Iris recognition system is the most secured, and faster means of identification and authentication. However, iris recognition system suffers a setback from blurring, low contrast and illumination due to low quality image which compromises the accuracy of the system. The acceptance or rejection rates of verified user depend solely on the quality of the image. In many cases, iris recognition system with low image contrast could falsely accept or reject user. Therefore this paper adopts Histogram Equalization Technique to address the problem of False Rejection Rate (FRR) and False Acceptance Rate (FAR) by enhancing the contrast of the iris image. A histogram equalization technique enhances the image quality and neutralizes the low contrast of the image at normalization stage. The experimental result shows that Histogram Equalization Technique has reduced FRR and FAR compared to the existing techniques.

Image feature extraction is one of the basic works for biometric analysis. This paper presents the novel concept of application of ridgelets for iris recognition systems. Ridgelet transforms are the combination of Radon transforms and Wavelet transforms. They are suitable for extracting the abundantly present textural data that is in an iris. The technique proposed here uses the ridgelets to form an iris signature and to represent the iris. This paper contributes towards creating an improved iris recognition system. There is a reduction in the feature vector size, which is 1X4 in size. The False Acceptance Rate (FAR) and False Rejection Rate (FRR) were also reduced and the accuracy increased. The proposed method also avoids the iris normalization process that is traditionally used in iris recognition systems. Experimental results indicate that the proposed method achieves an accuracy of 99.82%, 0.1309% FAR, and 0.0434% FRR.

High viscosity of quench oil is a critical problem of quench system in ethylene cracking furnaces in petrochemical plant, due to its influences on the safety and stability of equipments, meanwhile, the variety of viscosity of quench oil has a negative impact on yield of ethylene and other chemical products. This paper presents a new statistical learning model to forecast the real-time variety of quench oil viscosity based on statistical algorithm and machine learning method. Firstly, statistical algorithm is applied to reduce dimension of parameters, secondly, fitting real-time predictive model through machine learning method. The simulation results shows that this model can monitor the variety of viscosity per hour according to identified controllable parameters which are highly correlated with viscosity of quench oil.