Pupil Location Research Articles

The choice of the entrance pupil location in optical measurement of eyewear displays: Theoretical and experimental analyses

AbstractThe location of the entrance pupil, field of view, and motion of the viewer's eye is frequently discussed for virtual reality/augmented reality (VR/AR) displays. In order to properly measure optical performance of these displays, the light measuring device (LMD) must simulate the characteristics of the human eye. An important factor to consider is the location of the entrance pupil (aperture stop setting) of the LMD. We compared field of view (FOV) and luminance deviations with various pupil entrance locations at the LMD in this paper. As a result of the experiment, the entrance pupil at the front focal point of the LMD was the best choice.

Eye Pupil Localisation and Labeling Using a Small Size Database and YOLOv4 Object Detection Algorithm

Eye-related research has shown that eye gaze data are very important for applications that are essential to human daily life. Eye gaze data has been used in research and systems for eye movements, eye tracking and eye gaze tracking. Eye pupil localization, labelling and tracking are challenging problems in computer science. This article discusses and explores that problem․ The YOLOv4 (“You only look once”) object detection algorithm which is an evolution of the YOLOv3 model has been evaluated in a tiny database consisting of 103 eye images. The YOLOv4 algorithm was created by Alexey Bochkovskiy, Chien-Yao Wang and Hong-Yuan Mark Liao [3]. It is twice as fast as EfficientDet with comparable performance. The main purpose of this article is to test the YOLOv4 algorithm, to find out its effectiveness in process of localization and labelling of eye pupils and find out (determine) the effectiveness of the algorithm when training with a tiny database and with a relatively small number of iterations.

Research on image preprocessing for iris recognition

With the development of social economy, identity identification has penetrated into every aspect of people’s life. As the bottom technology of identity authentication, biometric technology is getting more and more attention in recent years. At present, iris based identification technology is a research hotspot with rapid development at home and abroad. Iris, as an important identification feature, has the advantages of uniqueness, stability, collectability and non-invasive. In this paper, the iris recognition algorithm is deeply studied, focusing on the image preprocessing in the iris recognition algorithm. In the aspect of positioning, the coarse to fine algorithm is used to ensure the accuracy and improve the positioning speed. The pupil location is divided into three steps. Based on the accurate pupil location, the iris is located in one step, and the effectiveness of the location is tested.

Subjective measurement of the Stiles-Crawford effect of the first kind with variation in accommodation.

To measure the Stiles-Crawford effect of the first kind (SCE-I), corresponding to central vision, with innovative technology to evaluate changes in the directionality and photoreceptor alignment with accommodation. A uniaxial Maxwellian system (spot size in pupil 0.5mm diameter) was employed, incorporating a spatial light modulator to flicker at 2Hz between two 2.3° fields corresponding to test (peripheral pupil) and reference (pupil centre) positions. Participants determined thresholds at 13 positions along the horizontal pupil meridian by indicating if the test field was brighter or dimmer than the reference field. Thresholds were determined by a staircase procedure after four reversals at each pupil location. After pupil dilation, seven emmetropes were tested at 0 D to 6 D accommodation stimulus levels in 2 D intervals. Data were fit by the Gaussian function, both when the fits were unforced or forced to pass through the sensitivity expected for the reference point. Directionality (ρ) and peak location values were determined for unforced and forced fits. Regression slopes for ρ as a function of accommodation stimulus were not significant. There was a tendency for to shift temporally with increasing accommodation across the 6 D stimulus range. This was not significant for regression fitting (-0.059mm/D, R2 =0.06, p=0.20), but a paired t-test for 0 and 6 D stimuli showed a weakly significant change of 0.62mm (p=0.05). The differences between the two fitting approaches were small and non-significant. Directionality did not change with accommodation, but the pupil peak location showed a significant temporal shift of approximately 0.62mm with 6 D accommodation stimulus. It is possible that substantial changes in the directionality and a shift in the direction of peak location might occur at very high levels of accommodation.

19.3: Evaluation of Entrance Pupil Location in Measuring VR\AR Eyewear Displays: Theoretical and Experimental Analyses in Field of View

The conventional display measurements are no longer satisfying the need for eyewear displays. The measurement of VR/AR type display requires light measuring devices (LMDs) to simulate the characteristics of the human eye. Factors like the position of the pupil, field of view, and the motion of the eye are often discussed. One of the factors to be considered is the choice of entrance pupil location (aperture stop setting) of the LMD. In this paper, a preliminary study was carried out to compare the deviations of FOV and luminance with the choice of entrance pupil location. The experiment demonstrated that it exists as the most accurate eye‐viewing when the entrance pupil is located at the front focal point.

A Pupil Segmentation Algorithm Based on Fuzzy Clustering of Distributed Information.

Pupil segmentation is critical for line-of-sight estimation based on the pupil center method. Due to noise and individual differences in human eyes, the quality of eye images often varies, making pupil segmentation difficult. In this paper, we propose a pupil segmentation method based on fuzzy clustering of distributed information, which first preprocesses the original eye image to remove features such as eyebrows and shadows and highlight the pupil area; then the Gaussian model is introduced into global distribution information to enhance the classification fuzzy affiliation for the local neighborhood, and an adaptive local window filter that fuses local spatial and intensity information is proposed to suppress the noise in the image and preserve the edge information of the pupil details. Finally, the intensity histogram of the filtered image is used for fast clustering to obtain the clustering center of the pupil, and this binarization process is used to segment the pupil for the next pupil localization. Experimental results show that the method has high segmentation accuracy, sensitivity, and specificity. It can accurately segment the pupil when there are interference factors such as light spots, light reflection, and contrast difference at the edge of the pupil, which is an important contribution to improving the stability and accuracy of the line-of-sight tracking.

Low-Complexity Pupil Tracking for Sunglasses-Wearing Faces for Glasses-Free 3D HUDs

This study proposes a pupil-tracking method applicable to drivers both with and without sunglasses on, which has greater compatibility with augmented reality (AR) three-dimensional (3D) head-up displays (HUDs). Performing real-time pupil localization and tracking is complicated by drivers wearing facial accessories such as masks, caps, or sunglasses. The proposed method fulfills two key requirements: low complexity and algorithm performance. Our system assesses both bare and sunglasses-wearing faces by first classifying images according to these modes and then assigning the appropriate eye tracker. For bare faces with unobstructed eyes, we applied our previous regression-algorithm-based method that uses scale-invariant feature transform features. For eyes occluded by sunglasses, we propose an eye position estimation method: our eye tracker uses nonoccluded face area tracking and a supervised regression-based pupil position estimation method to locate pupil centers. Experiments showed that the proposed method achieved high accuracy and speed, with a precision error of <10 mm in <5 ms for bare and sunglasses-wearing faces for both a 2.5 GHz CPU and a commercial 2.0 GHz CPU vehicle-embedded system. Coupled with its performance, the low CPU consumption (10%) demonstrated by the proposed algorithm highlights its promise for implementation in AR 3D HUD systems.

A Digital Microscreen for the Enhanced Appearance of Ocular Prosthetic Motility (an American Ophthalmological Society Thesis)

This study aims to improve the apparent motility of ocular prosthetic devices using technology. Prevailing ocular prostheses are acrylic shells with a static eye image rendered on the convex surface. A limited range of ocular prosthetic movement and lack of natural saccadic movements commonly causes the appearance of eye misalignment that may be disfiguring. Digital screens and computational systems may obviate current limitations in eye prosthetic motility and help prosthetic wearers feel less self-conscious about their appearance. We applied convoluted neural networks (CNNs) to track pupil location in various conditions. These algorithms were coupled to a microscreen digital prosthetic eye (DPE) prototype to assess the ability of the system to capture full ocular ductions and saccadic movements in a miniaturized, portable, and wearable system. The CNNs captured pupil location with high accuracy. Pupil location data were transmitted to a miniature screen ocular prosthetic prototype that displayed a dynamic contralateral eye image. The transmission achieved a full range of ocular ductions and with grossly undetectable latency. Lack of iris and sclera color and detail, as well as constraints in luminosity, dimensionality and image stability limited the real eye appearance. Yet, the digitally rendered eye moved in the same amplitude and velocity as the native, tracked eye. Real-time image processing using CNNs coupled to microcameras and a miniscreen DPE may offer improvements in amplitude and velocity of apparent prosthetic eye movement. These developments, along with ocular image precision, may offer a next-generation eye prosthesis. NOTE: Publication of this article is sponsored by the American Ophthalmological Society.

Comparative analysis of treatment zone diameter, location and amount of maximum defocus and pupil size with orthokeratology

Purpose: The purpose of this study was to retrospectively examine data from young patients who had undergone orthokeratology (OK) for myopia control to quantify the size and amount of central (treatment zone) and mid-perpheral (reverse zone) total refractive power (TRP) changes and compare where these changes occur with respect to the pupil size (PS) and location.

Pupil location algorithm applied to infrared ophthalmic disease detection

Pupil location algorithm applied to infrared ophthalmic disease detection

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.

Eye pupil localization algorithm using convolutional neural networks

Eye pupil localization is one of the indispensable technologies in various computer vision applications such as virtual reality and augmented reality. In general, the algorithm consists of finding the approximate eye region and finding the pupil position by extracting the semantic feature from each eye region. However, the performance of the eye pupil location is affected not only by illumination and image resolution but also by glasses wear. Therefore, this paper proposes an eye pupil localization algorithm which is robust against the above disturbance conditions and provides high accuracy. First, a face is detected from an input image and it is determined whether to wear glasses using the detected face. If glasses are present, the glasses are removed to find the correct eye region. Then, facial landmarks are extracted, and eye regions are detected based on facial landmarks. Next, the pupil region is segmented using fully convolutional networks. Finally, the position of the segmented pupil is calculated. Experimental results show that the proposed algorithm outperforms the state-of-the-art algorithms for public databases such as BioID and GI4E by up to 3.44% 0.5%, respectively.

Pupil Localisation and Eye Centre Estimation Using Machine Learning and Computer Vision.

Various methods have been used to estimate the pupil location within an image or a real-time video frame in many fields. However, these methods lack the performance specifically in low-resolution images and varying background conditions. We propose a coarse-to-fine pupil localisation method using a composite of machine learning and image processing algorithms. First, a pre-trained model is employed for the facial landmark identification to extract the desired eye frames within the input image. Then, we use multi-stage convolution to find the optimal horizontal and vertical coordinates of the pupil within the identified eye frames. For this purpose, we define an adaptive kernel to deal with the varying resolution and size of input images. Furthermore, a dynamic threshold is calculated recursively for reliable identification of the best-matched candidate. We evaluated our method using various statistical and standard metrics along with a standardised distance metric that we introduce for the first time in this study. The proposed method outperforms previous works in terms of accuracy and reliability when benchmarked on multiple standard datasets. The work has diverse artificial intelligence and industrial applications including human computer interfaces, emotion recognition, psychological profiling, healthcare, and automated deception detection.

Distinct differences in anterior chamber configuration and peripheral aberrations in negative dysphotopsia.

To provide insights into the anatomical characteristics associated with negative dysphotopsia using quantitative clinical data. Department of Ophthalmology, Leiden University Medical Center, Leiden, the Netherlands. Case-control study. Anterior chamber tomography and peripheral aberrometry were measured in 27 pseudophakic patients with negative dysphotopsia and 30 pseudophakic control subjects. Based on these measurements, the total corneal power, anterior chamber depth, pupil location and diameter, iris tilt, and peripheral ocular wavefront up to 30 degrees eccentricity were compared between both groups. In addition, ray-tracing simulations using pseudophakic eye models were performed to establish a connection between these clinical measurements and current hypotheses on the etiology of negative dysphotopsia. Twenty-seven patients with negative dysphotopsia and 25 pseudophakic controls were included in the analysis. The patients with negative dysphotopsia had a smaller (P = .03/P = <.01) and more decentered (P < .01) pupil than that of the pseudophakic controls. In addition, an increased temporal-tilted iris (P < .01) and an asymmetric peripheral aberration profile were observed in patients with negative dysphotopsia, of which the latter was also apparent in several ray-tracing models. The combination of these in vivo results and ray-tracing simulations indicated that patients with negative dysphotopsia had a temporal-rotated eye, which confirmed the hypothesized relation between negative dysphotopsia and an increased angle κ. Patients with negative dysphotopsia had a smaller pupil and an increased angle κ, which made them more susceptible to experiencing a shadow in the temporal visual field.

Research on Eye Pupil Location and Eye Tracking by Designing a Fully Conventional Neural Network (FCNN)

Research on Eye Pupil Location and Eye Tracking by Designing a Fully Conventional Neural Network (FCNN)

Design of corneal cross-linking system: with real-time positioning of pupil position

This paper presents a design of a corneal cross-linking instrument with the function of tracking and locating the central position of the pupil in real time. According to the application requirements, based on the principle of uniform light of compound eye lens, the optical projection system of the corneal crosslinking instrument with the diameter of the treatment spot being 9mm and the uniformity being greater than 90% was designed. And put forward a kind of the pupil localization algorithm, the acquisition of the pupil image gray processing and calculate the histogram, according to the characters of histogram adaptively to take out the appropriate threshold binarization, again after the binarization of image contour tracking, get accurate boundary, the outline of the final vote based on the sliding arc length increment method to determine the pupil center and radius. Experimental results show that the corneal cross-linking system designed in this paper can not only accurately project the treatment of light spot, but also accurately track the central position of the pupil of the human eye.

Exit pupil localization to correct spectral shift in thin-film Fabry-Perot spectral cameras

One of the key concerns in thin-film filter based spectral cameras is the presence of spectral shift in the measurements. This shift is caused by the sensitivity of the filters to the angle of incidence. In previous work, we showed that this shift can be corrected using a mathematical model. This model, however, requires knowledge of the distance to the exit pupil of the lens, which is not always readily available. We present a new model-based approach to estimate the distance to the exit pupil based on the observed spectral shift, making the method relevant for any thin-film Fabry-Perot based camera design. To implement the method, only a standard spectral camera setup and a well chosen spectral target are required. We also discuss how to optimally select such a target.

Intelligent Evaluation of Strabismus in Videos Based on an Automated Cover Test

Strabismus is a common vision disease that brings about unpleasant influence on vision, as well as life quality. A timely diagnosis is crucial for the proper treatment of strabismus. In contrast to manual evaluation, well-designed automatic evaluation can significantly improve the objectivity, reliability, and efficiency of strabismus diagnosis. In this study, we have proposed an innovative intelligent evaluation system of strabismus in digital videos, based on the cover test. In particular, the video is recorded using an infrared camera, while the subject performs automated cover tests. The video is then fed into the proposed algorithm that consists of six stages: (1) eye region extraction, (2) iris boundary detection, (3) key frame detection, (4) pupil localization, (5) deviation calculation, and (6) evaluation of strabismus. A database containing cover test data of both strabismic subjects and normal subjects was established for experiments. Experimental results demonstrate that the deviation of strabismus can be well-evaluated by our proposed method. The accuracy was over 91%, in the horizontal direction, with an error of 8 diopters; and it was over 86% in the vertical direction, with an error of 4 diopters.

An FPGA-Based Real-Time Human Eye Pupil Detection System Using E2V Smart Camera

Threshold based pupil detection algorithm was found tobe most efficient method to detect human eye. An implementation of a real-time system on an FPGA board to detect and track a human's eye is the main motive to obtain from proposed work. The Pupil detection algorithm involved thresholding and image filtering. The Pupil location was identified by computing the center value of the detected region. The proposed hardware architecture is designed using Verilog HDL and implemented on aAltera DE2 cyclone II FPGA for prototyping and logic utilizations are compared with Existing work. The overall setup included Cyclone II FPGA, a E2V camera, SDRAM and a VGA monitor. Experimental results proved the accuracy and effectiveness of the hardware realtime implementation as the algorithm was able to manage various types of input video frame. All calculation was performed in real time. Although the system can be furthered improved to obtain better results, overall the project was a success as it enabled any inputted eye to be accurately detected and tracked.

Human Eye Pupil Detection Technique Using Circular Hough Transform

Eye tracking refers to measure gaze positions and movement to reveal what individuals are looking at. Thanks to the advances of eye tracking technology, there are growing numbers of research focus in using eye tracking to study human behavior. In order to improve the accuracy of the eye gaze tracking technology, this paper presents a novel pupil detection algorithm based on intensity level with canny edge detection technique. Field programmable logic array (FPGA) based hardware implementation of the proposed technique is presented, which can be used in iris localization system on FPGA based platforms for iris recognition application.Threshold based pupil detection algorithm was found to be most efficient method to detect human eye. An implementation of a real-time system on an FPGA board to detect and track a human’s eye is the main motive to obtain from proposed work. The Pupil detection algorithm involved thresholding and image filtering. The Pupil location was identified by computing the center value of the detected region.