Iris Localization Algorithm Research Articles

Retracted: Iris Localization Algorithm based on Effective Area

Retracted: Iris Localization Algorithm based on Effective Area

Novel Multimodal Biometric Feature Extraction for Precise Human Identification

In recent years, biometric sensors are applicable for identifying important individual information and accessing the control using various identifiers by including the characteristics like a fingerprint, palm print, iris recognition, and so on. However, the precise identification of human features is still physically challenging in humans during their lifetime resulting in a variance in their appearance or features. In response to these challenges, a novel Multimodal Biometric Feature Extraction (MBFE) model is proposed to extract the features from the noisy sensor data using a modified Ranking-based Deep Convolution Neural Network (RDCNN). The proposed MBFE model enables the feature extraction from different biometric images that includes iris, palm print, and lip, where the images are preprocessed initially for further processing. The extracted features are validated after optimal extraction by the RDCNN by splitting the datasets to train the feature extraction model and then testing the model with different sets of input images. The simulation is performed in matlab to test the efficacy of the modal over multi-modal datasets and the simulation result shows that the proposed method achieves increased accuracy, precision, recall, and F1 score than the existing deep learning feature extraction methods. The performance improvement of the MBFE Algorithm technique in terms of accuracy, precision, recall, and F1 score is attained by 0.126%, 0.152%, 0.184%, and 0.38% with existing Back Propagation Neural Network (BPNN), Human Identification Using Wavelet Transform (HIUWT), Segmentation Methodology for Non-cooperative Recognition (SMNR), Daugman Iris Localization Algorithm (DILA) feature extraction techniques respectively.

Iris Recognition Method Based on Parallel Iris Localization Algorithm and Deep Learning Iris Verification.

Biometric recognition technology has been widely used in various fields of society. Iris recognition technology, as a stable and convenient biometric recognition technology, has been widely used in security applications. However, the iris images collected in the actual non-cooperative environment have various noises. Although mainstream iris recognition methods based on deep learning have achieved good recognition accuracy, the intention is to increase the complexity of the model. On the other hand, what the actual optical system collects is the original iris image that is not normalized. The mainstream iris recognition scheme based on deep learning does not consider the iris localization stage. In order to solve the above problems, this paper proposes an effective iris recognition scheme consisting of the iris localization and iris verification stages. For the iris localization stage, we used the parallel Hough circle to extract the inner circle of the iris and the Daugman algorithm to extract the outer circle of the iris, and for the iris verification stage, we developed a new lightweight convolutional neural network. The architecture consists of a deep residual network module and a residual pooling layer which is introduced to effectively improve the accuracy of iris verification. Iris localization experiments were conducted on 400 iris images collected under a non-cooperative environment. Compared with its processing time on a graphics processing unit with a central processing unit architecture, the experimental results revealed that the speed was increased by 26, 32, 36, and 21 times at 4 different iris datasets, respectively, and the effective iris localization accuracy is achieved. Furthermore, we chose four representative iris datasets collected under a non-cooperative environment for the iris verification experiments. The experimental results demonstrated that the network structure could achieve high-precision iris verification with fewer parameters, and the equal error rates are 1.08%, 1.01%, 1.71%, and 1.11% on 4 test databases, respectively.

A low complexity Iris localization algorithm for Iris biometrics

A low complexity Iris localization algorithm for Iris biometrics

Iris Localization Algorithm based on Effective Area

Iris localization is the most crucial part of the iris processing because its accuracy can directly affect the accuracy of biometric identification in subsequent steps. Yet, the quality of iris images may be sharply degraded due to interference from eyelashes and reflections during image acquisition, which can affect the localization accuracy adversely. To solve the problem, an iris localization algorithm based on effective area is proposed. First, YOLOv4 is used to crop the image to obtain the effective iris area, which is beneficial in improving the accuracy of subsequent localization. Furthermore, a method to remove reflective noise is proposed, which can effectively avoid the problem of noise interference in the process of inner boundary determination. Finally, aiming at the edge deviation caused by eyelashes, an outer boundary adjustment method is proposed. The experimental results show that the proposed method achieves good performance in the localization of iris images of both good quality and noise interference and outperforms other state-of-the-art methods.

Iris Location Algorithm Based on Union‐Find‐Set and Block Search

In view of the problem of unstable recognition effect and low robustness of a traditional iris location algorithm, an iris location algorithm based on union‐find‐set and block search is proposed. Firstly, the inner circle of the iris is roughly positioned by the method of retrieval, and then, the Hough transform is used to accurately locate the pupil. After that, the convolution operation is used to roughly locate the outer circle, and then, the original image is partitioned to search. And the grayscale change in the gray histogram of the screenshot is observed to accurately locate the outer circle. The obtained iris and the iris obtained by the traditional localization algorithm are processed by the same iris recognition algorithm. The results show that the proposed image is more effective in image recognition and has good robustness.

Dedicated hardware architecture for localizing iris in VW images

This study presents dedicated hardware for iris localization that can be used as a coprocessor in the development of real-time and low-cost embedded iris biometric systems. Though the hardware architecture is described for iris localization in the visible wavelength (VW) images, the concept used can be applied to near infrared (NIR) images as well. In general, the architecture can be used for a class of iris localization algorithms based on the edge-map generation and circular Hough transform (CHT). The architecture presented here generates the edge-maps for limbic and pupil boundary detection using median filtering followed by Sobel edge detection; however, an additional reflection removal module is used for pupil boundary detection. Further, the CHT hardware module detects circle in each edge-map. The proposed architecture was implemented in programmable logic of the Zynq-7000 SoC device from Xilinx. This hardware implementation gives an iris localization accuracy of 98.43% and average processing time of 5.148 ms for UBIRIS.v1 VW database images (200 × 150 pixel). The algorithm used is suitable for less unconstrained and frontal-view iris images captured with subjects’ active participation; however, the images may contain non-ideal issues such as reflection and occlusion by eyelids and eyelashes.

Fast iris localization algorithm on noisy images based on conformal geometric algebra

In practical iris applications, the obtained iris images are inevitably affected by noises brought by uneven illumination, off-angle view, eyelids, and eyelashes etc. Such influences can lead to poor accuracy and low efficiency of iris localization. This paper presents a novel conformal geometric algebra (CGA) based algorithm for accurate and fast iris localization. Firstly, two thresholds are obtained adaptively to convert the gray image into three-value image and Sobel edge detector is used to generate the edge points. Then an improved CGA-based circle detection algorithm is applied to detect the limbic and pupillary boundaries of the iris. Candidate boundaries are found with a priori knowledge of the eye structure. Finally, the CGA inner product is used to decide whether an edge point from candidate boundaries is on the circle or not. A defect ratio is defined to represent the completeness of each candidate boundary, and only those with the lowest defect ratio are set as the pupillary and limbic boundaries. Our algorithm detects candidate boundaries that satisfy a priori constraints rapidly and ensures the accuracy of iris localization. Experimental results on different datasets demonstrate the effectiveness and robustness of the algorithm.

Fast Iris Localization Based on Image Algebra and Morphological Operations

The localization of the iris is the most significant factor in biometrics of the iris, which traditionally assumes strictly controlled environments. The proposed method includes the pupillary and limbic iris boundaries localization. A primary advantage of image arithmetic operations is that the process is straightforward and therefore fast, these characteristics are employed and combined with the morphological operators in the designing of the proposed algorithm. The proposed algorithm takes into account the noise area which is found in various parts of the eye image such as light reflections, focus, and small visible iris. The experimental results are conducted on a collection of iris images consist of 756 images belong to Chinese Academy of Sciences Institute of Automation (CASIA V-1) and 450 images belong to Multi Media University (MMU V-1) databases. The results indicate a high level of accuracy using the proposed technique. Moreover, the comparison results with the state-of-the-art iris localization algorithms expose considerable improvement in segmentation accuracy while being computationally more efficient.

Sensitivity Analysis of a Class of Iris Localization Algorithms to Blurring Effect

This paper presents a study of a class of iris localization algorithms in the presence of blurring. The effect of blurring is a serious problem in most image processing systems. It may originate in iris imaging systems due to out-of-focus effect. It affects the features extracted from the iris images. Hence, the objective of this paper is to study the sensitivity of three popular iris localization algorithms to the presence of blurring. Features are extracted from normal as well as blurred iris images and used for iris localization. Moreover, Wiener filter restoration is used as a tool to combat the effect of blurring. Performance of the compared iris localization algorithms with Wiener filter restoration is also studied. Simulation results reveal that Masek iris localization algorithm has the least sensitivity to the blurring effect. Its accuracy without blurring is 88.2%, and with blurring, it decreases to 68.18%. Moreover, the Wiener filter significantly improves the accuracy of iris localization.

Iris Location Algorithm Based on the CANNY Operator and Gradient Hough Transform

In the iris recognition system, the accuracy of the localization of the inner and outer edges of the iris directly affects the performance of the recognition system, so iris localization has important research meaning. Our iris data contain eyelid, eyelashes, light spot and other noise, even the gray transformation of the images is not obvious, so the general methods of iris location are unable to realize the iris location. The method of the iris location based on Canny operator and gradient Hough transform is proposed. Firstly, the images are pre-processed; then, calculating the gradient information of images, the inner and outer edges of iris are coarse positioned using Canny operator; finally, according to the gradient Hough transform to realize precise localization of the inner and outer edge of iris. The experimental results show that our algorithm can achieve the localization of the inner and outer edges of the iris well, and the algorithm has strong anti-interference ability, can greatly reduce the location time and has higher accuracy and stability.

Efficient Iris Localization via Optimization Model

Iris localization is one of the most important processes in iris recognition. Because of different kinds of noises in iris image, the localization result may be wrong. Besides this, localization process is time-consuming. To solve these problems, this paper develops an efficient iris localization algorithm via optimization model. Firstly, the localization problem is modeled by an optimization model. Then SIFT feature is selected to represent the characteristic information of iris outer boundary and eyelid for localization. And SDM (Supervised Descent Method) algorithm is employed to solve the final points of outer boundary and eyelids. Finally, IRLS (Iterative Reweighted Least-Square) is used to obtain the parameters of outer boundary and upper and lower eyelids. Experimental result indicates that the proposed algorithm is efficient and effective.

Non-circular iris contours localization in the visible wavelength eye images

Contemporary iris localization schemes perform well for ideal eye images generally acquired using near infrared (NIR) illumination, but perform poorly for non-ideal eye images acquired using Visible Wavelength (VW) illumination. Non-ideal eye images contain noise such as non-uniform illumination, defocus, etc. This study proposes a reliable iris localization algorithm, in particular for VW eye images. First, it suppresses light reflections and sharp intensity variations in the Red-component of input color eye image. Next, it marks a set of potential pixels (ROIpixels) suitable as the iris center's candidates. Using ROIpixels, it localizes potential iris location using a hybrid of Integro-differential operator (IDO) and image intensity. Then, it reuses IDO and iris/pupil's geometry to localize pupil within iris. Finally, it uses the Fourier series based scheme to regularize iris contours. Experimental results obtained on UBIRIS V1.0, MMU V1.0, and IITD V1.0 iris databases demonstrate its superior performance compared with contemporary schemes.

High Biometric Recognition Based on Histogram and Semi-discrete Matrix Decomposition via Neural Network

Iris recognition is regarded as the most reliable and accurate biometric identification system available highly protected and stable.Iris situating is the main focus in the procedure of iris recognition and verifies the precision of identification. In this work, a new algorithmfor iris localization is suggested based on the median filter and the histogram to determine an automated global threshold and the pupil centre. An algebraic based on semi-discrete matrix decomposition SDD is used to extract iris feature from iris image that decrease the difficultyininput layered neural network,thesizes of input patterns are enhanced. The iris recognition is developed by a neural network with differential adaptive learning rate to identify the iris features. This method is simple,effective and high speed recognition. The system is implemented by using Matlab. Experimental outcomes indicate that the suggested algorithm gives the accuracy of 100 % with time equal 1.4 sec is best than other methods for Daugman and Wildes.

An Iris Localization Algorithm based on Geometric Features of the Circle

An Iris Localization Algorithm based on Geometric Features of the Circle

Research on Iris Localization Algorithm based on the Active Contour Model

An automatic and robust iris segmentation algorithm will be employed when iris recognition is used to identify human efficiently. Many studies have shown that iris localization is one of the most important parts of the iris recognition system. Most current studies are based on the assumption that iris edge is round, and the gray information of eye image is used for iris segmentation, which requires very high quality of image. Based on the active contour model, an algorithm for iris location was proposed. First, a plurality of pupil edge points was located as the initial contour point set. Then, the real contour evolution of pupil was approximated by the approximation of energy function. Finally, the accurate iris edge was located by daugman’s circular gradient operator. Based on CASIA (V2.0) iris database, experiment results show that the location effect of the proposed algorithm is more superior than that of the classical calculus method and hough transform method.

An Iris Localization Algorithm based on Morphological Processing

To improve the speed of iris localization, an iris localization algorithm based on the morphological processing is proposed with fast speed. Firstly, pupil area is segmented from eye image by thresholding, to remove eyelash noise and other noises from binaryzation pupil area by morphological open operation. Then, a series of structure element of radius increasing is used to make morphological erode operation on pupil area to localize roughly the inner boundary of the iris. Finally, calculus operator is employed to accurately localize the inner and outer iris boundary. 108 iris images from CASIA (Version 1.0) iris database are used to do iris localization experiments. The localization accurate rate of the proposed algorithm, calculus operator and hough transform is 97.2%, 90.3% and 92.1% respectively. Experiment results have showed that the proposed algorithm has a high performance on speed and precision with strong robustness to the different quality iris images.

Comparison of Two Iris Localization Algorithms

Iris recognition is regarded as a most reliable and accurate biometric identification system. Daugman’s Integrodifferential operator is a linear search method which makes the identification process extremely slow as well as increases the false acceptance rate beyond an acceptable range. The present work uses distance regularized level set evolution (DRLSE) method on CASIA-V3-Interval database and applies a suitable algorithm to detect the iris from an image. The two techniques i.e., Daugman’s Integro-differential operator and DRLSE are compared based on accuracy and time taken to localize the iris.

Iris Recognition using Robust Localization and Nonsubsampled Contourlet Based Features

The conventional iris recognition methods do not perform well for the datasets where the eye image may contain nonideal data such as specular reflection, off-angle view, eyelid, eyelashes and other artifacts. This paper gives contributions for a reliable iris recognition method using a new scale-, shift- and rotation-invariant feature-extraction method in time-frequency and spatial domains. Indeed, a 2-level nonsubsampled contourlet transform (NSCT) is applied on the normalized iris images and a gray level co-occurrence matrix (GLCM) with 3 different orientations is computed on both spatial image and NSCT frequency subbands. Moreover, the effect of the occluded parts is reduced by performing an iris localization algorithm followed by a four regions of interest (ROI) selection. The extracted feature set is transformed and normalized to reduce the effect of extreme values in the feature vector. Next, significant features for iris recognition are selected by a two-step method composed by a filtering stage and wrapper based selection. Finally, the selected feature set is classified using support vector machine (SVM). The proposed iris identification method was tested on the public iris datasets CASIA Ver.1 and CASIA Ver.4-lamp showing a state-of-the-art performance.

An Improved Iris Localization Algorithm Based on Curve Fitting

Iris localization is to detect outer-and-inner boundaries of iris in an iris image. In the paper, an improved algorithm was proposed to quickly and effectively locate outer-and-inner boundaries. As for this algorithm, the first is to block an iris image and extract its sub-image blocks which cover pupil; the second is to set a binary threshold of pupil by adopting the method of Maximum Variance between Clusters; the third is to get the value outer-boundary-points of iris, on the basis of gray gradient of key Regions-of-interest; the last is to select some characteristic pixels in regions of interest respectively and fit outer-and-inner boundaries of iris according to curve fitting.