Iris Localization Research Articles

A method for extracting corneal reflection images from multiple eye images

The incident light reflected from the cornea is rich in information about the human surroundings, and these reflected rays are imaged by the camera, which can be used for research on human consciousness and gaze analysis, and produce certain help in the fields of psychology, human computer interaction and disease diagnosis. However, limited by the low corneal reflection ability, when a high-definition camera captures corneal reflecting rays, a large amount of color and texture interference from the iris can seriously contaminate the corneal reflection images, resulting in low usability and ubiquity of corneal reflection images. In this paper, we propose a corneal reflection image extraction method with multiple eye images as input. We align the iris regions of multiple eye images with the help of iris localization method, and by comparing multiple iris regions, we obtain the complementary iris regions, so that the iris interference in the corneal reflection region can be stripped completely. A large number of experiments have demonstrated that our work can effectively mitigate iris interference and effectively improve the quality of corneal reflection images.

Retracted: Iris Localization Algorithm based on Effective Area

Retracted: Iris Localization Algorithm based on Effective Area

An Application Study of Improved Iris Image Localization Based on an Evolutionary Algorithm

This study aims to enhance the localization of the inner and outer circles of the iris while addressing issues of excessive invalid computations and inaccuracies. To achieve this objective, diverse methods are employed to improve the process to varying extents. Initially, the image undergoes pre-processing operations, including grayscale conversion, mathematical morphological transformation, noise reduction, and image enhancement. Subsequently, the accurate localization of the inner and outer edges is achieved by applying algorithms such as Canny edge detection and the Hough transform, allowing for the determination of their corresponding center and radius values within the iris image. Lastly, an improvement is made to the particle swarm optimization algorithm by combining various algorithms, namely LinWPSO, RandWPSO, contraction factor, LnCPSO, and AsyLnCPSO, employing mechanisms such as simulated annealing and the ant colony algorithm. Through dual validation on the CASIA-Iris-Syn dataset and a self-built CASIA dataset, this approach significantly enhances the precision of iris localization and reduces the required iteration count.

Iris Data Compression Based on Hexa-Data Coding

Iris research is focused on developing techniques for identifying and locating relevant biometric features, accurate segmentation and efficient computation while lending themselves to compression methods. Most iris segmentation methods are based on complex modelling of traits and characteristics which, in turn, reduce the effectiveness of the system being used as a real time system. This paper introduces a novel parameterized technique for iris segmentation. The method is based on a number of steps starting from converting grayscale eye image to a bit plane representation, selection of the most significant bit planes followed by a parameterization of the iris location resulting in an accurate segmentation of the iris from the original image. A lossless Hexadata encoding method is then applied to the data, which is based on reducing each set of six data items to a single encoded value. The tested results achieved acceptable saving bytes performance for the 21 iris square images of sizes 256x256 pixels which is about 22.4 KB on average with 0.79 sec decompression average time, with high saving bytes performance for 2 iris non-square images of sizes 640x480/2048x1536 that reached 76KB/2.2 sec, 1630 KB/4.71 sec respectively, Finally, the proposed promising techniques standard lossless JPEG2000 compression techniques with reduction about 1.2 and more in KB saving that implicitly demonstrating the power and efficiency of the suggested lossless biometric techniques.

The discrete wavelet transform based iris recognition for eyes with non-cosmetic contact lens

Iris recognition has been used as one of the biometric systems for user authentication, identification, and verification for quite some time. The basis of an iris recognition lies on the matching algorithm, which requires similarities of the iris data in the database with the captured one. In addition, nowadays using non-cosmetic or prescribed contact lenses becomes more popular and more preferred choice of many people, which makes the number of contact lens wearers significantly increases. These eyes with contact lenses add more complexity to the iris recognition process, since it can disturb the matching process which then affect the performance of the system. This situation has motivated this study to propose an iris recognition system that works for eyes with lenses. The proposed iris recognition system for eyes wearing non-cosmetic contact lenses was developed using circular hough transform (CHT) in the preprocessing phase for iris localization and discrete wavelet transform (DWT) for feature extraction. Experiments conducted on the proposed system has shown promising results with good accuracy of 0.95 for eyes with no contact lens and 0.8 for eyes with non-cosmetic lenses. The findings also suggested the importance of the iris localization process to the performance of the recognition system.

Double-Center-Based Iris Localization and Segmentation in Cooperative Environment with Visible Illumination.

Iris recognition has been considered as one of the most accurate and reliable biometric technologies, and it is widely used in security applications. Iris segmentation and iris localization, as important preprocessing tasks for iris biometrics, jointly determine the valid iris part of the input eye image; however, iris images that have been captured in user non-cooperative and visible illumination environments often suffer from adverse noise (e.g., light reflection, blurring, and glasses occlusion), which challenges many existing segmentation-based parameter-fitting localization methods. To address this problem, we propose a novel double-center-based end-to-end iris localization and segmentation network. Different from many previous iris localization methods, which use massive post-process methods (e.g., integro-differential operator-based or circular Hough transforms-based) on iris or contour mask to fit the inner and outer circles, our method directly predicts the inner and outer circles of the iris on the feature map. In our method, an anchor-free center-based double-circle iris-localization network and an iris mask segmentation module are designed to directly detect the circle boundary of the pupil and iris, and segment the iris region in an end-to-end framework. To facilitate efficient training, we propose a concentric sampling strategy according to the center distribution of the inner and outer iris circles. Extensive experiments on the four challenging iris data sets show that our method achieves excellent iris-localization performance; in particular, it achieves 84.02% box IoU and 89.15% mask IoU on NICE-II. On the three sub-datasets of MICHE, our method achieves 74.06% average box IoU, surpassing the existing methods by 4.64%.

Measurement of 6D face pose and iris location using web camera

Measurement of 6D face pose and iris location using web camera

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.

Robust Iris-Localization Algorithm in Non-Cooperative Environments Based on the Improved YOLO v4 Model

Iris localization in non-cooperative environments is challenging and essential for accurate iris recognition. Motivated by the traditional iris-localization algorithm and the robustness of the YOLO model, we propose a novel iris-localization algorithm. First, we design a novel iris detector with a modified you only look once v4 (YOLO v4) model. We can approximate the position of the pupil center. Then, we use a modified integro-differential operator to precisely locate the iris inner and outer boundaries. Experiment results show that iris-detection accuracy can reach 99.83% with this modified YOLO v4 model, which is higher than that of a traditional YOLO v4 model. The accuracy in locating the inner and outer boundary of the iris without glasses can reach 97.72% at a short distance and 98.32% at a long distance. The locating accuracy with glasses can obtained at 93.91% and 84%, respectively. It is much higher than the traditional Daugman's algorithm. Extensive experiments conducted on multiple datasets demonstrate the effectiveness and robustness of our method for iris localization in non-cooperative environments.

Research on strabismus iris segmentation model based on deep snake multitask learning

Biometrics is widely used in various fields as a means of biological individual identification, and iris recognition is recognized as an efficient, stable, and secure biometric method. However, when the cooperation with users is reduced under complex scenes, the collected iris images are prone to squint or defocus, which causes serious difficulties for iris preprocessing, especially iris location, and segmentation. To solve this problem, we propose a multitask deep learning model based on deep snake. Using the closed-loop prior conditions of the inner and outer boundaries of the iris itself, the active contour method is used to achieve accurate iris location, and semantic segmentation is carried out in the effective region. First, the initial iris location and segmentation results are obtained using the target detection module based on mask regions with convolutional neural networks, then the initial contour is located by poles. The iris boundary is represented by a set of ordered point coordinates through equal spacing sampling. The initial contour is transferred as input to the active contour fitting network based on deep snake to obtain accurate iris location results. Finally, the segmentation results are obtained by the internal and external boundary constraint models. In the segmentation process, efficient channel attention mechanism is introduced to improve the segmentation accuracy and make the network focus on the effective iris region. The SoftPool layer is used to reduce the loss of image detail information caused by pooling, so that the whole network is differentiable. The performance improvements of 3.26%, 3.9%, and 3.45% are obtained on the three squint iris datasets, respectively, which proves the effectiveness of the model in solving the squint iris problem and improves the accuracy and robustness of iris recognition in complex scenes.

Dynamic iris inner edge location method based on distance-regularized level set

Iris localization is an important part of iris recognition. Aiming at the influence of irregular pupil circle and illumination inequality in dynamic iris localization in actual scene, a dynamic iris inner edge location method based on distance regularized level set model was proposed. According to the geometric features of iris, the pupil and iris are coarsely located through the secondary projection of binary image, and the initial contour of evolution of horizontal set is determined. Accurate positioning of the inner edge of iris is achieved by the improved distance regularized level set model of edge indicator function. Iris images Database were respectively experimented with this paper and another improved distance regularized level set model, and the positioning effect was evaluated by using the Jakard similarity coefficient. The results show that the distance regularized level set model based dynamic iris internal edge localization algorithm proposed in this paper can significantly reduce the number of iterations and positioning time of the localization algorithm on the basis of ensuring the positioning accuracy,which provides a theoretical basis and technical reference for the application of iris recognition technology.

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.

Review on Iris Recognition Research Directions- A Brief Study

Abstract : Biometric methods, which identify people based on physical or behavioral characteristics, are of interest because people cannot forget or lose their physical characteristics in the way that they can lose passwords or identity cards. Among these biometric methods, iris is currently considered as one of the most reliable biometrics because of its unique texture‘s random variation. It is found that this method for Iris Recognition design offers good class discriminacy. The iris recognition technique consists of iris localization, normalization, encoding and comparison. The Neural Classifier will be a feed forward network with three hidden layers and be used after normalization and feature extraction phase. Simulation results will be very promising in person identification.

Accurate Iris Segmentation for Non-Cooperative Iris Recognition through Multi-task Network

Iris image suffer from noise due to non-cooperative situation. It creates challenge for segmentation. Here we propose deep learning based efficient segmentation method. It provides complete solution for such situation. To train this we have used various databases, which involved various illumination and imaging sensors. The proposed segmentation method can be utilized for all iris recognition methodology to improve performance in non-cooperative iris recognition. Key words: Iris Segmentation, multi task learning, iris localization, iris recognition

Iris R-CNN: Accurate iris segmentation and localization in non-cooperative environment with visible illumination

Despite the significant advances in iris segmentation, accomplishing accurate iris segmentation and localization in non-cooperative environment with visible illumination remains a grand challenge. In this paper, we present an end-to-end multi-task deep neural network, referred to as Iris R-CNN, to achieve superior accuracy for iris segmentation and localization. Inspired by instance segmentation, Iris R-CNN seamlessly integrates segmentation and localization in a unified framework and generates a normalized iris image/mask required for iris recognition. It is composed of several novel techniques to carefully explore the unique characteristics of iris. First, we propose two novel networks, (i) Double-Circle Region Proposal Network (DC-RPN) and (ii) Double-Circle Classification and Regression Network (DC-CRN), to efficiently capture pupil and iris circles and enhance the accuracy for iris localization. Second, we propose a novel normalization scheme for Regions of Interest (RoIs) to enable a radically new pooling operation over a double-circle region. To facilitate accurate training and validation, we annotate two public datasets in non-cooperative environment with visible illumination: NICE-II and MICHE. Experimental results on these two challenging datasets demonstrate the superior accuracy of our proposed approach over other state-of-the-art methods.

A low complexity Iris localization algorithm for Iris biometrics

A low complexity Iris localization algorithm for Iris biometrics

An Improved Iris Localization Method

Iris research has become an inevitable trend in the application of identity recognition due to its uniqueness, stability, non-aggression and other advantages. In this paper, an improved iris localization method is presented. When the iris inner boundary is located, a method for extracting the iris inner boundary based on morphology operations with multi-structural elements is proposed. Firstly, the iris image is pre-processed, and then the circular connected region in the pre-processed image is determined, the parameters of the circular connected region is extracted, finally the center and the radius of the circular connected region is obtained, .i.e., the iris inner boundary is excavated. When the iris outer boundary is located, a method for locating iris outer boundary based on annular region and improved Hough transform is proposed. The iris image is first filtered, and then the filtered image is reduced and an annular region is intercepted, finally Hough transform is used to search the circle within the annular region, i.e., the center and the radius of the iris outer boundary is obtained. The experimental results show that the location accuracy rate of this proposed method is at least 95% and the average running time is increased by 46.2% even higher. Therefore, this proposed method has the advantages of high speed, high accuracy, strong robustness and practicability.

IMPROVING IRIS RECOGNITION ACCURACY USING GABOR KERNELS WITH NEAR-HORIZONTAL ORIENTATIONS

extraction of iris features. However, the selection of Gabor kernel orientations remains an important key for an optimum performance. In this paper, Gabor kernels with horizontal and two near-horizontal orientations (0°, 15°, 165°) are used for extracting iris features, as an attempt to improve the performance of iris recognition rate. The three iris features are cascaded into one image for the purpose of image matching. To this end, an iris recognition system is developed using methods of iris localization, eyelid removal and iris matching by Hamming Distance (HD) that have been developed previously by the same authors. The system is implemented on three standard datasets, CASIA-1, CASIA-Lamp-4 and SDUMLA-HMT. The results have shown that the overall accuracy (Accu), the True Positive Rate (TPR) and the Equal Error Rate (EER) EER are (98.85%, 99.42% and 0.58%) for CASIA-1 database, (96.56%, 98.25% and 1.75%) for CASIA-Lamp-4 database and (96%, 98% and 2.0%) for SDUMLA-HMT database. These results outrage the results of most of the previous works that have used the same databases.

Chaotic Krill Herd with Deep Transfer Learning-Based Biometric Iris Recognition System

Biometric verification has become essential to authenticate the individuals in public and private places. Among several biometrics, iris has peculiar features and its working mechanism is complex in nature. The recent developments in Machine Learning and Deep Learning approaches enable the development of effective iris recognition models. With this motivation, the current study introduces a novel Chaotic Krill Herd with Deep Transfer Learning Based Biometric Iris Recognition System (CKHDTL-BIRS). The presented CKHDTL-BIRS model intends to recognize and classify iris images as a part of biometric verification. To achieve this, CKHDTL-BIRS model initially performs Median Filtering (MF)-based preprocessing and segmentation for iris localization. In addition, MobileNet model is also utilized to generate a set of useful feature vectors. Moreover, Stacked Sparse Autoencoder (SSAE) approach is applied for classification. At last, CKH algorithm is exploited for optimization of the parameters involved in SSAE technique. The proposed CKHDTL-BIRS model was experimentally validated using benchmark dataset and the outcomes were examined under several aspects. The comparison study results established the enhanced performance of CKHDTL-BIRS technique over recent approaches.

Detection and Localization of Iris using Digital Image Processing Techniques

Detection and Localization of Iris using Digital Image Processing Techniques