Iris Center Research Articles

Exploiting Eye Colors for Better Iris Segmentation in Visible Wavelength Environments

Iris segmentation is a crucial step in iris recognition systems. Iris segmentation in visible wavelength and unconstrained environments is more challenging than ‎segmenting iris images in ideal environments. This paper proposes a new iris segmentation method that exploits the color of human eyes to segment the iris region more accurately. While most of the current iris segmentation methods ignore the color of the iris or deal with ‎grayscale eye images directly, the proposed method takes benefits from iris color to simplify the iris segmentation step. In the first step, we estimate the expected iris center using Haar-like features. The iris color is detected and accordingly, a color-convenient segmentation algorithm is applied to find the iris region. Dealing separately with each iris color set significantly decreases the false segmentation errors and enhances the performance of the iris recognition system. The results of testing the proposed algorithm on the UBIRIS database demonstrate the robustness of our algorithm against different noise factors and non-ideal conditions.

Pupil Position by an Improved Technique of YOLO Network for Eye Tracking Application

This Eye gaze following is the real-time collection of information about a person's eye movements and the direction of their look. Eye gaze trackers are devices that measure the locations of the pupils to detect and track changes in the direction of the user's gaze. There are numerous applications for analyzing eye movements, from psychological studies to human-computer interaction-based systems and interactive robotics controls. Real-time eye gaze monitoring requires an accurate and reliable iris center localization technique. Deep learning technology is used to construct a pupil tracking approach for wearable eye trackers in this study. This pupil tracking method uses deep-learning You Only Look Once (YOLO) model to accurately estimate and anticipate the pupil's central location under conditions of bright, natural light (visible to the naked eye). Testing pupil tracking performance with the upgraded YOLOv7 results in an accuracy rate of 98.50% and a precision rate close to 96.34% using PyTorch.

A cascaded deep learning framework for iris centre localization in facial image

AbstractAccurate iris centre localization is crucial in many computer vision and facial biometric applications such as gaze estimation, human–computer interaction, iris recognition, and liveness detection. However, it is challenging in an uncontrolled environment due to variations like pose, scale, rotation, specular reflection, and image quality. Therefore, a cascaded deep learning framework for iris centre localization in facial images is proposed that is robust to the abovementioned variations. The proposed approach consists of (i) YOLOv3 for eye detection, (ii) UNet for iris segmentation, and (iii) statistical modelling for iris centre localization. The eyes are first detected using the YOLOv3, and subsequently, iris segmentation is performed within the detected eyes using the UNet. Following iris segmentation, statistical modelling is employed to enhance the localization accuracy of the iris centre. Experiments were performed on benchmark databases, resulting in a standardized error measure SED of 3.405 pixels for BioID and 3.259 pixels for GI4E databases. In addition, the robustness of the proposed eye detection model was further evaluated on the Yale B for illumination variations and the CAS‐PEAL for pose variations.

Real-time Iris Center Detection Based on Convolutional Neural Networks

It is an active field of study in studies where the iris center is referenced, such as iris center detection, gaze tracking, driver fatigue detection. In this study, an approach for real-time detection of iris centers based on convolutional neural networks is presented. The GI4E dataset was used as the dataset for the proposed approach. Experimental results estimated the test data of the proposed convolutional neural network model with an accuracy of 97.2% based on the 0.025 error corresponding to the closest position to the iris center according to the maximum normalized error criteria. The study was also tested in real time with a webcam built into the computer. While the test accuracy is satisfactory, real-time speed performance needs to be improved.

Contribution of cornea to the peripheral ocular refraction in the two-dimensional visual field

The evaluation of the contact lenses for myopia control is often focused on the change in corneal refractive power, which is applied to indicate the contribution of the cornea to the peripheral ocular refractions (POR). However, this concept lacks a direct relationship to the POR for a realistic human eye with structural and optical irregularities. In this study, we propose a mathematically rigorous and highly efficient procedure for specifying the contribution of the cornea to the POR based on the wavefronts propagating along the chief rays from the iris center to the entrance pupil center. Using generalized ray tracing, the complexity in the impacts of the geometry of both corneal surfaces and their orientation within the human eye are fully explained with analytical equations. As a case study, the proposed procedure is performed on a realistic corneal model constructed from the measurement data on children. An approximately linear relationship has been found between the POR and the vergence of the wavefronts at the iris center for all the visual field locations. Based on this finding, we find that the contribution of the cornea to the POR can be summarized by the slope and intercept parameters derived from the linear fits in the two-dimensional visual field. The proposed analysis procedure provides a powerful tool for evaluating the peripheral optical effects of the contact lens design.

Laboratory and Clinical Experience With a Diffractive Trifocal Intraocular Lens Sutured to an Artificial Iris.

To determine in vitro, using a translational research approach before realizing the procedure in a patient with iatrogenic aphakia and partial aniridia, whether suturing a trifocal intraocular lens (IOL) to an artificial iris degrades the IOL's optical quality. Optical quality was analyzed by measuring the modulation transfer function (MTF) at a 3-mm aperture and at 50 and 100 lp/mm spatial frequencies. The FineVision Pod F GF IOL (PhysIOL) was assessed in two powers: two +20.00 diopters (D) (20A and 20B IOLs) and two +30.00 D (30A and 30B IOLs). The IOLs' decentration in relation to the artificial iris's center was evaluated. The laboratory results provided empirical evidence in the informed consent for surgical intervention in a patient with iatrogenic aphakia and iris defect in one eye. Clinical results were measured using the parameter of corrected distance visual acuity plus a patient self-assessment of the cosmetic appearance of the operated eye. The 20A and 20B IOLs demonstrated a mean MTF reduction of up to 1.1%, whereas the 30A and 30B IOLs showed a decrease of up to 5.2% for both spatial frequencies. All lenses showed good centration levels. In the clinical case, the patient showed corrected distance visual acuity, distance-corrected near visual acuity, and distance-corrected intermediate visual acuity of 0.20, 0.20, and 0.22 logMAR, respectively. The patient was satisfied with the cosmetic outcome. There was merely a slight reduction in trifocal IOL optical quality after it was sutured to an artificial iris. Clinically, the combined implantation of the artificial iris and FineVision IOL provided good functional and cosmetic outcomes. [J Refract Surg. 2022;38(1):61-68.].

A Method for Accurate 3-D Gaze Estimation With a Single Camera and Two Collinear Light Sources

The simplest configuration for estimating 3D line-of-sight based on corneal reflection and pupil refraction uses a single camera and two non-collinear light sources. The corneal radius and 3D corneal center can be determined by solving a system of six equations with six unknowns. However, when two light sources are collinear with the camera, the reflection planes of the two light sources are coplanar. In this case, the estimation of 3D corneal center is under-determined in the absence of any pre-known eyeball parameters (e.g., corneal radius). To solve this issue, this paper proposes to calibrate the corneal radius with corneal reflections from two light sources combined with an iris imaging model, so that the 3D corneal center is estimated with a known corneal radius. Then, the pupil features or iris features can be selected to estimate the 3D pupil center or 3D iris center, which uses an optimization solution for only the center parameters to avoid unnecessary errors. Thus, the optical axis of the eyeball is constructed with two spatial centers, and the kappa angle calculated in real time is used to convert the optical axis of the eyeball to the 3D line-of-sight. We simulated this gaze estimation process using pupil and iris features, and performed error analysis on the proposed method. In practical experiments, the proposed method was verified using iris features, and the gaze accuracy was 1.22° and 1.13° in the x- and y-directions, respectively, which is comparable to that of more sophisticated systems. A single-camera-two-collinear-light-source system is the most typical and common system configuration, and the realization of 3D gaze estimation under such a system configuration can effectively promote the wide application of eye-tracking interaction.

EllSeg: An Ellipse Segmentation Framework for Robust Gaze Tracking.

Ellipse fitting, an essential component in pupil or iris tracking based video oculography, is performed on previously segmented eye parts generated using various computer vision techniques. Several factors, such as occlusions due to eyelid shape, camera position or eyelashes, frequently break ellipse fitting algorithms that rely on well-defined pupil or iris edge segments. In this work, we propose training a convolutional neural network to directly segment entire elliptical structures and demonstrate that such a framework is robust to occlusions and offers superior pupil and iris tracking performance (at least 10% and 24% increase in pupil and iris center detection rate respectively within a two-pixel error margin) compared to using standard eye parts segmentation for multiple publicly available synthetic segmentation datasets.

Evaluation of accurate iris center and eye corner localization method in a facial image for gaze estimation

Accurate estimation of eye-related information is important for many applications such as gaze estimation, face alignment, driver drowsiness detection, etc. Earlier works fail to estimate eye information in low-resolution images captured by a regular camera or webcam. This paper is aimed at developing an Iris Center (IC) and Eye Corner (EC) localization method in low-resolution facial images with an application of gaze estimation. A three-stage method is proposed for IC and EC localization. In the first stage, a circular gradient-intensity-based operator is proposed for rough ICs estimation and a CNN model is used in the second stage to find true ICs. In the third stage, Explicit Shape Regression (ESR) method is used for EC localization where initialization is done taking the ICs as a reference point to the mean eye contour shape model. The proposed IC localization method is evaluated on BioID and Gi4E database and it shows better accuracy compare to some of the state-of-the-art methods. This method further evaluated for gaze estimation based on IC and EC which does not require any prior calibrations unlike earlier infrared illumination-based gaze trackers. Here, the experiment for gaze estimation is performed in our proposed NITSGoP database that prepared under indoor conditions with complex background and uneven illuminations. The experimental results suggest that the proposed method can be used for gaze estimation with better accuracy both in still images and videos.

Eye Tracking and ROI Detection within a Computer Screen Using a Monocular Camera

The region of interest will change according to the task, even in the same situation. In the study, a method for region of interest detection within a computer screen using a monocular camera is proposed. In contrast to gaze tracking techniques that require particular devices (e.g., an eye tracker and RGB-D device) meanwhile complex calibration, a cheap and more convenient monocular camera is used in this study to solves the eye gaze tracking problem. Firstly, Human face is detected in a real-time video sequence using HoG features. Then, the landmarks around the eyes, which reflect the gaze position, are extracted. Next, the iris centers are detected in the eye region. In order to reduce the gaze error caused by head movement, a three-dimensional head model is proposed to estimate head pose. Finally, the eye region is tracked by calculating the eye vectors and head movement. Experiments were performed to evaluate the face detection, landmarks, iris detection, eye movement estimation, and head pose estimation on databases such as the Hong Kong, BioID, and Boston University head pose databases. Besides, experiments for gaze tracking were performed for a real-time video sequence. Deviation is calculated using Euclidean distance between the real and estimated points. The results show that the method achieves an average error of 1.85∘ with head fixed and 3.58∘ with head movement in the range of −45∘ and 45∘. The requirement is detecting the user’s attention in the screen area. Our method can reach the same level to the other methods, even though the accuracy is not state-of-the-art. Meanwhile, as we all know not only a specific point is concerned but also a region area according to the characteristics of human eye imaging, thus the proposed method can meet the requirements of demand.

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%.

A New Approach for Sclera Segmentation Using Integro Differential Operator

Sclera vessel patterns are one of the most reliable biometrics against performance, accessibility and availability. Sclera segmentation is the important phase of sclera recognition system as its performance depends on the segmentation accuracy. The big challenge in segmentation is nonlinearity of iris, sclera and upper and lower eyelid. This paper proposes a new approach to isolate the sclera accurately from the unconditionally acquired eye image. Hough transform and Integro differential operator (IDO) are popularly used to confine the iris and sclera boundary. Both of these two localizations are done based on the midpoint of the iris. These techniques produce better results as compared with other approaches but computation of iris center point incurs more time. To overcome this time consumption issue gabor based filter has been used. Experimental results are listed based on the UBIRIS database images. The experimental result shows that the proposed segmentation system will produce fast and accurate result.

Iris Recognition Method for Non Ideal Images

The issue of counterfeiting is one of the most pressing threats to people’s security because of the many complex security problems. Many systems used in the field of verification and identification but the basic criterion for choosing is the degree of safety identification. Identification and verification systems use biometric characteristics of individuals. The system of iris recognition is one of the most important biometric recognition systems. The system used iris images for 100 persons belonging to the UBIRIS V1 database. Each person has 5 captures; the total number is 500 images. A hybrid iris segmentation method combined two algorithms ChanVese (CV) and grabcut to obtain high verification rate and to detect the outer boundary and localize the iris region. The system includes several steps starting with acquiring the image of the iris, enhancing the image quality, segment iris region, determining the coordinates of iris center and radius of the pupil, convert the iris coordinates of the cutting segments from Cartesian coordinates to the polar coordinates to reduce the processing time and to avoid the noise image problems due to eyelids and eyelashes, then utilize Gabor wavelets to extract the features of iris, and finally perform the matching process using Euclidian distance. The performance indicators, which include the recognition rate, false rejection rate(FRR), False Acceptance Rate(FAR), Correct identification rate(CIR), are as follows(97.5%, 39%, 0.025%, 0.0975). The proposed system has many advantages includes high processing capability of low-quality images and low storage capacity.

Iris center localization using energy map synthesis based on gradient and isophote

Iris center localization using energy map synthesis based on gradient and isophote

Appearance-based Gaze Estimation Using Multi-task Neural Network

In order to improve the gaze estimation accuracy through using the information in the eye image more adequately, an appearance-based gaze estimation neural network EyeNet has been proposed in this article. On the one hand, by decomposing the main task into two subtasks, this network could gain more information during the training stage. On the other hand, EyeNet could extract features in the image more efficiently by adding an auxiliary task which is detecting the iris center and eye ball center of the eye. Experiments show that by using these two tricks, the error decreased from 5.42 degree to 5.16 degree.

Using an Automated Hirschberg Test App to Evaluate Ocular Alignment.

A smartphone app has been developed to perform the automated photographic Hirschberg test for objective measurement of ocular misalignment. By computing the difference in corneal reflection generated by the phone camera flash relative to the iris center based on high resolution images, the app can measure misalignment with a much higher precision than the naked eye performing the Hirschberg test. It has been validated in a previous clinical evaluation study by comparing to the clinical gold standard-prism and alternate cover test. The goal of this article is to describe the testing techniques regarding how to use the app to measure ocular alignment for different fixation distances, without or with cover to break fusion, as well as angle kappa, so that users can use the app to perform equivalent tests typically done in clinic using prisms.

GazMon: Eye Gazing Enabled Driving Behavior Monitoring and Prediction

Automobiles have become one of the necessities of modern life, but also introduced numerous traffic accidents that threaten drivers and other road users. Most state-of-the-art safety systems are passively triggered, reacting to dangerous road conditions or driving maneuvers only after they happen and are observed, which greatly limits the last chances for collision avoidances. Timely tracking and predicting the driving maneuvers calls for a more direct interface beyond the traditional steering wheel/brake/gas pedal. In this paper, we argue that a driver's eyes are the interface, as it is the first and the essential window that gathers external information during driving. Our experiments suggest that a driver's gaze patterns appear prior to and correlate with the driving maneuvers for driving maneuver prediction. We accordingly present GazMon, an active driving maneuver monitoring and prediction framework for driving assistance applications. GazMon extracts the gaze information through a front-camera and analyzes the facial features, including facial landmarks, head pose, and iris centers, through a carefully constructed deep learning architecture. Both our on-road experiments and driving simulator based evaluations demonstrate the superiority of our GazMon on predicting driving maneuvers as well as other distracted behaviors. It is readily deployable using RGB cameras and allows reuse of existing smartphones towards more safely driving.

Iris Center Localization Using Energy Map With Image Inpaint Technology and Post-Processing Correction

Iris center localization is the basis of iris biometrics, face recognition and gaze tracking. However, individual differences, changes in facial expression, varying light conditions, occlusion, and so on, all bring great challenges to accurately localize the iris center. In order to improve localization accuracy in low-quality images and meet the need of efficiency in practical applications, a novel method of iris center localization is proposed in this paper using energy map synthesis based on image gradient, image inpaint technology, and post-processing correction. The image inpaint technology is firstly adopted to inhibit the effect of some specular reflection. Then the energy maps based on image gradient and eye ROI (Region Of Interest) midpoint are synthesized to significantly improve the localization accuracy. In the end, post-processing correction is carried out to eliminate influence of the closed eye and other large derivations to further improve the localization accuracy. The algorithm is verified on the challenging BioID database, Talking Face Video database and the MUCT face database. The result shows the localization accuracy has outperformed the state-of-the-art unsupervised methods on the three databases, and it is suitable for real-time applications.

Detection of Iris Localization in Facial Images Using Haar Cascade Circular Hough Transform

In numerous science applications, face detection and iris extraction have been recognized as crucial stages by getting more consideration among researchers as it has an important job. This paper presents an automatic detection method of the iris and its center detection by applying the Haar Cascade Classifier and the Circular Hough Transform algorithm. The suggested method is divided into two primary methodologies: face recognition utilizes the Haar Cascade Classifier and iris extraction using the Hough Transform. The system detects the face from a set of facial images using an Impa-faced dataset. The improved AdaBoost algorithm constructs a cascaded classifier for face detection. Then, by applying the Haar Cascade to obtain an eye pair region and a Hough transform for iris detection by extracting Haar features. Finally, the improved circular Hough transform algorithm locates the iris center. The experimental results of the suggested method show a high-speed, robust ability to acquire the coordinates of the iris center accurately under various illumination changes on different states of human images. The overall accuracy for locating the iris center was 98.75%.

Cornea Radius Calibration for Remote 3D Gaze Tracking Systems

Cornea radius estimation is a key technique for 3D gaze estimation in the single-camera 3D gaze tracking system. Traditional methods with one-camera-one-light-source systems or one-camera-two-light-source systems cannot achieve 3D gaze estimation. The 3D line-of-sight can be estimated only when the cornea radius is pre-calibrated by the user. A cornea radius calibration method based on the iris radius is proposed in this paper for 3D gaze estimation in remote one-camera-two-light-source systems. We first calibrate the iris radius based on the binocular strategy, estimate the spatial iris center using the calibrated iris radius, and then calibrate the cornea radius by a set of non-linear equations under the constraint of equivalent distances from the cornea center to the iris edge points. The calibrated cornea radius is verified by binocular optimization constraints. Simulations and physical experiments validate the effectiveness of the proposed method. The iris-based cornea radius calibration approach is novel; it can be used to obtain the cornea radius and 3D gaze using remote one-camera-one-light-source or one-camera-multi-light-source systems.