Iris Localization Research Articles

Are IoBT services accessible to everyone?

Biometric recognition aims at identifying a person by using their physiological or behavioral characteristics. When adopted for improving the security in the Internet of Things (IoT) field, it is commonly named Internet of Biometric Things (IoBT). However, despite its advantages there are further considerations on security and different ethical and legal issues, such as the possibility of exclusion of individuals due to pathologies, injuries, disabilities, or genetic defects. Indeed, these specific physical condition would lead to not satisfy the requirements commonly used for biometric recognition. As a consequence, the limitations of current biometric systems can exclude a person from the use of IoBT services. In this paper, we focus on the difficulty of iris recognition when it is affected by Coloboma, a congenital abnormality of membranes of the eye. We show how this pathological state impacts on the performance of the Daugman and Canny edge detection algorithms , which represent the most widespread methods used for the iris localization step in eye-based biometric. Results of an experimentation revealed that they correctly detected only 15.79% and 47.37% of Coloboma iris, respectively. In order to avoid the use of these inaccurate algorithms in case of Coloboma eye, we designed and experimented a Residual Neural Network classifier able to detect the presence of this disease with 99.79% of accuracy. This classifier may be a first step towards a more sophisticated “diversity-aware” biometric system which represents an alternative to actual IoBT authentication method for people with special physical condition.

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.

A review of techniques of iris replication and a novel method of fabrication of ocular prosthesis using two different iris location methods.

Anophthalmic patients suffer from social, functional, and emotional agony. The role of a maxillofacial prosthodontist is to restore this loss and to restore the self-esteem of such patients. Fabrication of a prosthesis for the anophthalmic cavity can be successful with the judicious use of the materials, the vision, and the skill of the prosthodontist. This article describes various techniques used for iris replication and a novel method to locate the iris, which is the most crucial step to fabricate a natural gaze.

Iris Localization using the Pupil Center Point based on Deep Learning in RGB Images

Iris Localization using the Pupil Center Point based on Deep Learning in RGB Images

Segmentation Approach for a Noisy Iris Images Based on Hybrid Techniques

Iris recognition indicates the procedure of recognizing humans based on their both left and right iris patterns. Nowadays there is rapid progress in realizing an old dream of developing a user-friendly recognition system. Most of the new projects became a nightmare of security of the system. The prosperity of iris recognition aside from its attractive physical characteristics is led to developing an efficient feature extractor to attain the required objective of recognition. Fingerprint, facial, and iris biometric techniques are developed widely for identifying processing most boarded management points, access control, and military checkpoints. Hybridization between Daugman’s Integro Differential Operator (IDO) with edge base methods was realized through taking the advantages of the good qualities of both methods so as to enhance the precision and reduce the required time. The proposed hybrid recognition system is very reliable and accurate. UBIRIS version 1 dataset was utilized in the conducted simulation which indicates the distinctions of the hybrid method in providing good performance and accuracy with reducing the time consuming of iris localization by approximately 99% compared with IDO and edge based methods.

IRIS LOCATION METHOD BASED ON MATHEMATICAL MORPHOLOGY AND IMPROVED HOUGH TRANSFORM

An iris location method based on mathematical morphology and improved Hough transform is proposed in this paper. When locating the iris inner boundary, an iris inner boundary method based on mathematical morphology and circle fitting is proposed. The iris image is first preprocessed to get a binary image by the gray stretch transform or the circular region mean filter, and then the type of the connected region selected as the iris inner boundary in the binary image is determined by the ratio of the width to the length. On the foundation, the different method is used to extract the parameters of the connected region and the iris inner boundary is obtained. When locating the iris outer boundary, an iris outer boundary method based on the improved Hough transform is put forward. First, the Gaussian filter is used to deal with the iris image, the filtered image is reduced, Canny operator is employed for edge detection, an annular region centered at the center of the iris inner boundary of the reduced image is cropped to explore a circle by Hough transform, and the center and the radius of the iris outer boundary are worked out. The experimental results reveal that this proposed method has the advantages of fast speed, high accuracy, strong robustness and practicability.

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.

A NEW IRIS LOCATION METHOD

A new iris location method is presented in this paper. When locating the iris inner boundary, a method for locating the iris inner boundary based on the variation coefficient and multiple disk-structural elements is proposed. The variation coefficient of the iris image is first calculated to be used for the image gray stretch, then the mathematical morphology operations with multiple disk-structural elements are applied to tackle the binary iris image, finally the connected region in line with geometric features of the circle is picked up and its center and radius are calculated, namely for obtaining the iris inner boundary. When positioning the iris outer boundary, a method for locating the iris outer boundary based on the variation coefficient and the gradient amplitude is presented. First, the Gaussian function is employed to filter the iris image using the variation coefficient as its standard deviation, then for each pixel on a circle which is centered at each neighborhood pixel near the circle enter of the iris inner boundary and has a radius with greater than that of the inner boundary, the radial gradient amplitude and tangential gradient amplitude of are computed, finally the iris outer boundary is determined on the maximum of the tangential gradient amplitude. The experiments reveal that this proposed method is effective, and has a fairly simple implementation, a low computational load, a fast location and good accuracy.

A Multi-Modal Gaze Tracking Algorithm

A person’s emotional information, needs and cognitive processes can be described by eye movement states and concerns, so gaze tracking was first applied in the field of psychology. With the continuous development of information technology, the application range of gaze tracking has expanded from psychology to medical, military, commercial and many other fields. Aiming at the problem of high misjudgment rate and long time-consuming of traditional iris location methods, this paper proposes a gaze tracking method based on human eye geometric characteristics to improve the tracking accuracy in 2D environment. First, the human face is located by face location algorithm and the position of human eye is estimated roughly. Then the iris template is built by iris image, and the iris center location algorithm is used to locate the iris center position. Finally, the eyes corners and iris center points are extracted to locate the eye area accurately and obtain the binocular image. The binocular images are input into the feature extraction network as multi-modal information in parallel, and the convoluted feature channels are reconstructed using the weight redistribution module in the network. Then the reconstructed features are fused in the full connection layer. Finally, the output layer is used to classify the reconstructed features. Experiments were carried out on a self-built screen block dataset. For 12 classified data, the lowest recognition error rate is 5.34%.

Research on iris location based on improved particle swarm algorithm and sub-regional noise reduction

Iris localization is the basis of iris recognition, iris medical diagnosis and other technical fields. In view of image of Asian iris, according to the characteristics of the human eye pupil, the combination method of image back-complement enhancement and improved particle swarm optimization is firstly adopted to fit and locate the inner circle of iris. Then, combined with the characteristics of the outer iris boundary, the outer iris circle is located by using the method of sub-regional noise reduction and improved particle swarm optimization. Meanwhile, coordinate transformation approach is introduced for normalization of image. Finally, the feasibility of the method is verified by using iris library of Chinese Academy of Sciences and the self-built iris library sample. This study successfully overcomes the interference of the spot in library and can efficiently complete the segmentation of the iris region.

A robust iris localization scheme for the iris recognition

Due to current security situations around the globe, iris biometric technology is highly preferred for both overt and covert applications. A typical iris biometric system includes image acquisition, iris segmentation, features extraction, and matching and recognition modules. Amongst these modules, iris segmentation plays a decisive role because it segments the valid iris part in an input eyeimage. It includes two tasks: iris localization and noise (e.g., eyelids) removal. Notably, the overall performance of an iris biometric system strongly relies on the iris localization task, because it demarcates the actual iris contours. Some contemporary iris localization schemes search over a three-dimensional (3D) space while marking iris boundaries, which is a time-consuming process if not optimized properly. Besides, some schemes also resort to the fixed and/or crude thresholding-based techniques for pupil localization. Notably, such schemes may perform poorly if image data do not maintain quality. To address these issues, this study proposes a robust iris localization scheme maintaining both speed and accuracy. It includes preprocessing the input eyeimage using an order statistic-filter and the bilinear interpolation scheme, extracting an adaptive threshold using the image’s histogram, processing binary image via the morphological operators, extracting pupil’s center and radius based on the centroid and geometry concepts, marking iris outer boundary using the Circular Hough transform (CHT) and refining coarse iris boundaries through the Fourier series. The proposed scheme exhibits relatively better experimental results compared with some contemporary iris localization schemes on the public iris databases: IITD V1.0, CASIA-Iris-Interval and MMU V1.0.

Investigation of iris segmentation techniques using active contours for non-cooperative iris recognition

<span>Iris image segmentation process based on graphical user interface (GUI) to accurately localize the iris structure is presented in this paper. The major challenge confronting the precision of an iris recognition model is how to determine the accuracy of the iris segmentation and localization. There are varying parameters that introduce constraints during feature extraction and these greatly affect the matching performance during iris localization. To this end, the Integro-differential operator, which involves the detection of inner and outer regions of the iris, and the circular hough transform, which is capable of detecting the circular boundary from the edge mapping were investigated, and an active contour model was evolved. In the evolved model, an emerging curve mapped with the zeros of the data set function is experimentally exploited. To demonstrate the suitability of the model for precise iris recognition, its parameters were compared against other related models. Simulation results show that the model has higher flexibility of substitution of images, and the images could be analyzed more accurately with less false rejections (FR) and false acceptance (FA) in comparison with the integro-differential operator. This implies that images could be analyzed faster using the evolved model, and easily substituted especially in situations where the need to care for numerous eye patients occur.</span>

Adaptive Multi Type of Interpolation for Iris Localization

Adaptive Multi Type of Interpolation for Iris Localization

An experimental study of relative total variation and probabilistic collaborative representation for iris recognition

Iris images collected under different conditions often suffer from specular reflections, cast shadows, motion blur, defocus blur, occlusion caused by eyelashes and eyelids, eyeglasses, hair and other artifacts. Existing iris recognition systems do not perform well on these types of images. To overcome these problems, an iris recognition method based on relative total variation (RTV) and probabilistic collaborative representation is proposed. RTV uses the l1 norm regularization method to robustly suppress noisy pixels to achieve accurate iris localization, while probability collaborative representation maximizes the probability that the test sample belongs to each of the multiple classes. The final recognition rate is calculated based on the class having maximum probability. Experimental results using CASIA-V4-Lamp and IIT-Delhi V1iris image databases showed that the proposed method achieved competitive performance in both recognition accuracy and computational efficiency.

Optimization method for non-cooperative iris recognition task using Daugman integro-differential operator

We considered the issues arising when Daugman’s integrodifferential operator is used to localize iris edges. Daugman’s integro-differential operator is a widely common method to localize the iris, however, problems of the operator optimization poorly presented in the literature. The article considers existing methods of optimization of the operator applied at the iris recognition step. Based on the provided research we proposed using Nelder-Mead and Differential Evolution methods to optimize the integro-differential operator. The problems of the iris localization and their solving methods were considered. The article focuses attention on non-cooperative iris recognition. The very general boundary conditions based on iris’s anatomy which are not dependent on captured image properties were defined. The results of the comparative analysis of the accuracy and performance of selected optimization methods of Daugman’s integrodifferential operator were presented at the experimental results chapter. It was found that the Differential evolution optimization method gives fine performance and correctness. It was concluded that the Differential evolution is expedient as Daugman’s integro-differential operator optimization method.

Iris Image Segmentation and Localization using Dynamic Reconfigurable Processor

Recognition of the Iris is among the finest techniques in the field of bio-metrics identification, because the iris has characteristics that are unique and stay the same all through the individual’s life. Iris recognition phases are namely image acquisition, segmentation of iris, localization of iris, feature extraction of iris and matching. This paper, which is an extension of the survey paper Smaran et.al[1], concentrates purely on the procedures of image capture, segmentation as well as localization of the iris. The aim of the paper is to optimize the above mentioned processes in terms of distance of capturing the image, time taken for memory and computation requirements, using the DRP (Dynamic Re-Configurable Processor) technology, uniquely developed by Renesas Electronics (www.renesas.com).

An effective iris segmentation scheme for noisy images

Iris segmentation plays a critical role in the iris biometric systems. It has two modules: iris localization and noise detection. The first module demarcates the actual iris’ inner and outer boundaries in input eyeimages. The second module detects and removes noise in the valid iris part. Researchers devised numerous iris segmentation and/or localization schemes, which are based on the histogram and thresholding, circular Hough transform (CHT), Integro-differential operator (IDO), active contour models, graph-cuts, or deep learning. It is observed that most contemporary schemes perform poorly when confronted with images containing noisy factors such as the eyebrows, eyelashes, contact lenses, non-uniform illumination, light reflections, defocus and/or eyeglasses. The performance of CHT and IDO against noise is found robust, but these operators are computationally expensive. On the other hand, the histogram and thresholding-based schemes are considered fast, but these are less robust against noise. Besides, most contemporary schemes mark iris contours with a circle approximation and offer no noise removal strategy. To address these issues, this study offers an effective iris segmentation algorithm. First, it applies an optimized coarse-to-fine scheme based on an adaptive threshold to mark iris inner boundary. Next, it detects and marks eyelashes adaptively. After that, it marks iris outer boundary via an optimized coarse-to-fine scheme. Then, it regularizes the non-circular iris’ contours using the Fourier series. Finally, eyelids and reflections are marked in the iris polar form. The proposed scheme shows better results on the CASIA-Iris-Interval V3.0, IITD V1.0, and MMU V1.0 iris databases.

Fast Iris localization using Haar-like features and AdaBoost algorithm

Traditional iris recognition methods, which are still preferred against artificial intelligence (AI) approaches in practical applications, are often required to capture high-grade iris samples by an iris scanner for accurate subsequent processing. To reduce the system cost for mass deployment of iris recognition, pricey scan devices can be replaced by the average quality cameras combined with additional processing algorithm. In this paper, we propose a Haar-like-feature-based iris localization method to quickly detect the location of human iris in the images captured by low-cost cameras for the ease of post-processing stages. The AdaBoost algorithm was chosen as a learning method for training a cascade classifier using Haar-like features, which was then utilized to detect the iris position. The experimental results have shown acceptable accuracy and processing speed for this novel cascade classifier. This achievement stimulates us to implement this novel capturing device in our iris recognition.

Segmentation Approach for a Noisy Iris Images Based on Block Statistical Parameters

The Iris localization plays a big role in the performance of an iris recognition system. This is due to the dependent of the next steps up on it, and the incorrect segmentation might lead to inexact normalization and improper feature extraction from less discriminatory parts (eyelids, eyelashes, pupil, etc.) so the execution of system will diminish. An effective method for locating the iris of the eye is suggested in this paper. At first a mixture of gamma transform and contrast enhancing mechanisms are used to guarantee a precise renovation of eye image to become an iris area easy to isolate. The next step is relayed on calculating the statistical image parameters (i.e., the mean and standard deviation) which are employed as a feature to detect outer iris boundary. The integro-differential operational technique is used with further pre-processing processes to detect the inner boundaries of iris. The noisy iris UBIRIS.v1 dataset was used in the experiment. Thee conducted results indicated that the proposed technique has a good performance, which is improved accuracy of iris localization step for noisy dataset.