Suitable checkpoints of features surrounding the eye for eye tracking using template matching

Interpreting “statistical hypothesis testing” results in clinical research

The process of eye tracking has been subject of intensive research during the last years. The main aim consists in the determination of the point of gaze or the eye movement relative to the head. In this work, we use a static video camera to realize the tracking of the eyes movement mainly due to its important features such as low cost, fully automated and easy application. The proposed approach encompasses a solution to the problems of eye detection and eye tracking. Our eye detection method employs a set of cascaded Haar classifiers that enable the fast recognition of eye regions in a real-time sequence of images. On the other hand, the eye tracking method uses particle filtering that is robust in clutter environments and allows the fusion of sensor data as well as the tracking of nonlinear systems. The results demonstrate that the eye tracking system is capable of robustly track the eyes under variable light conditions and under several different face orientations.

In this paper, we propose an eye detection method that is robust to facial pose changes using gradient directional features of brightness and a particle filter. The rejection function of incorrect eye detection in the proposed method allows for the eye detection again even if the eye is incorrectly detected. In this method, to estimate the boundary between the iris and sclera or eyelid, the gradient intensities are calculated by four directional Prewitt filters in four regions. The likelihood used in the particle filter is obtained by averaging the gradient intensities for the specific direction in the four regions and the upper eyelid area. From experimental results, the average detection rates of both eyes for roll, yaw, and pitch angles of the face are more than 90% by using rejection function for incorrect eye detection. The rejection function produces the 4.4%, 4.5%, and 4.9% increases in average detection rates of both eyes for roll, yaw, and pitch facial angles, respectively. The proposed eye detection method can track both eye in real-time (about 20 ms) and is robust to the facial pose changes.

This study proposes a simple, affordable and available technology for controlling the computer cursor with only your eyes. The proposed design method was designed and developed for people with significant upper limb disabilities that don’t use a keyboard or a mouse, and has been framed as a standard input method re-defining the notion of input by an eye-re-URL sing interface. Based upon a standard web camera and real-time image processing, the eye tracking system uses the direction of an individual’s facial gaze and blinking detect and translate that into the movement needed for the cursor action. The proposed eye tracking system is lighter and less accessible because it is not dependent upon sophisticated input-device hardware, thus able to obtain reliable cursor movement whenever the lit conditions in the environment are suitable to allow the eye-tracking input system to function smoothly. Based upon early trials, there has been a reported ease-of-use and high responsivity to potential use of a system using assistive technology capabilities. Keywords— Eye tracking ,Cursor control, Webcam interface Blink detection, Physically disabled

Statistical hypothesis testing is one of the key steps in modern medical research. Initially, scientists formulate a research hypothesis based on which the statistical hypothesis is then developed and statistically tested. This review provides the null and alternative hypotheses’ compiling examples for different research questions and the general algorithm for their testing using t-test. The authors also describe type I errors, which are necessary to interpret p-values estimated from statistical tests, and type II errors, which are used to assess study power. The article focuses on effect size and its calculation methods, and the difference between statistically significant and clinically significant effects. The associations between effect size, sample size, and type II error are also discussed.

Real-time human eye recognition and tracking systems with human-computer interaction mechanism are being adopted to advance user experience in smart devices and consumer electronic systems. Eye tracking systems measure eye gaze and pupil response non-intrusively. This paper presents an analysis of pupil response to video structure and content. A consumer device that can assess user response to a video can provide better experiences with approaches such as real-time content adaptation. The first set of experiments involved presenting different video content to subjects and measuring eye response with an eye tracker. Results show that pupil constrictions and magnitude of constrictions vary with content. Significant changes in video and scene cuts led to sharp constrictions. User response to videos can provide insights that can improve subjective quality assessment metrics. This paper also presents an analysis of the pupillary response to quality changes in videos with the second set of experiments. The results of the tests show pupil constrictions for noticeable changes in perceived quality. Using real-time eye tracking systems for video analysis and quality evaluation can open a new class of applications for consumer electronic systems.

This paper describes a flexible method for synchronizing electroencephalography (EEG) and eye tracking (ET) recordings to the presentation of visual stimuli. The method consists of embedding a synchronization signal in the visual stimuli, and recording this signal with both the EEG and ET equipment. The signal is recorded by the EEG device as an additional data channel, and with the camera used in the ET equipment by modulating the global illumination of the scene in time with the synchronizing signal. The prototype system where this method was implemented resulted in a single sample of jitter in both the EEG and ET system, while the ET system achieved a spatial resolution of 1.26 degrees. The system will be used in future work with augmented memory applications.

Eye tracking has become an essential human–machine interaction modality in virtual reality (VR) and augmented reality (AR) applications requiring high throughput (e.g., more than 240 frames per second), small form factor, and enhanced visual privacy. Existing eye tracking systems have adopted bulky, lens-based cameras, and thus suffer from both a large form factor and high communication cost between the camera and back-end processor. This work presents a camera, algorithm, and accelerator co-designed lensless eye tracking system dubbed EyeCoD, which, to the best of our knowledge, is the first to provide a general, front-end eye tracking solution for AR/VR while satisfying the requirements for both high throughput and smaller form factor. Specifically, EyeCoD integrates system-, algorithm-, and accelerator-level techniques to boost system efficiency without sacrificing eye tracking accuracy. We believe that our EyeCoD system will pave the way for next-generation eye tracking solutions in VR/AR and shed light on future innovations for intelligent imaging systems.

We propose a novel method of eye detection that is robust to obstacles such as the surrounding illumination, hair, glasses and etc. The obstacles above the face images are the constraints to detect eye position. These constraints affect the performance of face application systems such as face recognition, gaze tracking, and video indexing system. To overcome this problem, our method for eye detection consists of three steps. In preprocess, we apply SQI (Self Quotient Image) to the face images to reduce illumination effect. Then, we extract the eye candidates by using the gradient descent which is simple and fast computing method. Finally, the classifier which has trained by using AdaBoost algorithm selects the eyes from all of the eye candidates. The usefulness of proposed method has been demonstrated in experiments with the eye detection performance.

For more than three decades, Deborah Mayo has defended severe testing, a framework for evaluating the strength of scientific evidence that provides philosophical foundations for (variants of) classical statistical methods. In her newest book, Statistical Inference as Severe Testing, Mayo (1) extends and refines her views on severe testing, (2) takes aim at researchers who claim that the reproducibility crisis is a result of the use of classical statistics, and (3) exposes some contemporary Bayesian methods as lacking philosophical foundations. The book is engaging, sometimes funny, and often insightful. After reviewing the basics of severe testing, I summarize three lessons from Mayo's book that are valuable for philosophers and scientists alike. I then discuss one way in which I think Mayo's arguments could be tightened.Mayo argues that one has evidence for a hypothesis to the extent the hypothesis has passed a severe test. More precisely, she argues that “for data to warrant a hypothesis H requires not just that (S-1) H agrees with the data (H passes the test), but also (S-2) with high probability, H would not have passed the test so well, were H false” (92). Mayo's two criteria are inspired by the Neyman-Pearsonian maxim that good statistical tests minimize Type 1 and Type 2 errors, but they are novel in at least two ways. First, Mayo's criteria are applicable outside of statistical contexts. For example, let H be the hypothesis that the Cleveland Browns played an excellent team last weekend, and suppose our data is that the Cleveland Browns lost their game. If the Cleveland Browns lose to all opponents regardless of ability, then our data fails to satisfy S-2 and so does not severely test H. Since S-1 and S-2 are applicable in both statistical and nonquantitative contexts, Mayo's criteria unify the logic of statistical testing with qualitative logics of evidence.Second, severity is a function of data, unlike size and power, the two core quantities of orthodox Neyman-Pearsonian methodology. For this reason, Mayo's two criteria closely resemble Nozick's adherence and sensitivity conditions when those conditions are relativized to “methods” (Nozick 1981).1Mayo draws several important lessons from this seemingly simple, two-pronged framework. Here, I discuss three that recur throughout the newest book. First, Mayo carefully distinguishes between statistical hypotheses (e.g., about the frequency of a trait in a population) and “substantive” scientific hypotheses about, for example, causation or the existence of a theoretical entity (84, 93). Conflating the two can lead one to mistakenly infer that a substantive scientific hypothesis has been severely tested, when in fact the evidence against the hypothesis is stronger than that for it.As a vivid example, Mayo discusses a series of psychological experiments used to defend the hypothesis H that heterosexual “men's implicit self-esteem is lower when a [female] partner succeeds than when a [female] partner fails” (101). Mayo carefully distinguishes four statistical hypotheses that are plausibly consequences of ¬ H and then argues that the data were sufficient to reject only one of the four; call it ¬ H 1. Mayo concludes that the experimental data failed to fit H, as researchers could not reject three statistical hypotheses that were compatible with ¬ H. Further, Mayo argues that the statistical evidence against ¬ H 1 (i.e., the purported evidence for ¬¬ H = H ) was perfectly compatible with the falsity of the substantive hypothesis H. So the data also failed to satisfy S-2.Second, Mayo argues that, whereas the severe tester has a unified account of testing both statistical hypotheses and modeling assumptions, likelihoodists and Bayesians lack a cogent story about when to reject a bad statistical model (298–320). Some likelihoodists, for example, argue that measures of evidential strength must be comparative and that the likelihood ratio P (E|H 0)/P (E|H 1) quantifies the degree to which evidence E fits a hypothesis H 0 better than H 1 (Edwards 1984; Royall 1997). It is easy to construct cases in which such a likelihood ratio is enormous, because the data fit H 0 much better than H 1, and yet the data fit neither statistical hypothesis well. If H 0 and H 1 are the only statistical hypotheses under investigation, one might erroneously conclude that one has good evidence for H 0. The right conclusion, according to the severe tester, is to recognize that the two hypotheses do not exhaust logical space and to reject that underlying modeling assumption instead.Naive Bayesian reasoning falls victim to the same criticism. According to some Bayesians, experimenters ought to assign prior probabilities to a fixed set of hypotheses H and then update their degrees of belief by conditionalization. Again, suppose the data fit all hypotheses in H poorly but fit one H ∈ H much better than its rivals. Then a naive Bayesian's posterior degree of belief in H might be nearly one. Again, the right conclusion, according to the severe tester, is to consider more hypotheses. But orthodox Bayesianism is silent about when to do that.Finally, Mayo argues that several novel Bayesian approaches to statistical inference lack foundations (400–415). These parts of the book will be of special interest to philosophers who are unfamiliar with the substantial differences between Bayesianism in theory and in practice. According to textbook philosophical presentations, prior and posterior probabilities represent an experimenter's beliefs. However, some contemporary Bayesians have given up on the myth that the infinitely precise densities that appear in journal articles are appropriate idealizations of any person's beliefs. Those Bayesians, however, disagree about what justifies the choice of prior (e.g., transparent communication vs. frequentist coverage properties) and the use of further Bayesian mathematical machinery.Mayo levels several different criticisms against these contemporary Bayesian approaches, but I think all share the same underlying philosophical problem. Rationality, so says standard decision theory, requires one to maximize expected utility relative to one own's degrees of belief. So some contemporary “Bayesian” methods require a new theory of rationality to justify the behavior of an experimenter whose decisions employ posterior probabilities that do not even approximately represent her (or perhaps anyone's) beliefs.There are other important lessons scattered through Mayo's engaging and fairly accessible book. That leads me to my main criticism: accessibility sometimes comes at the cost of precision. I focus on one example that is directly relevant to Mayo's analysis of the causes of the replicability crisis, a central topic of the book.Mayo repeatedly suggests that a hypothesis is not severely tested if it is the result of “data-dredging.” Data-dredging is one of several pejorative terms (including p-hacking) that some statistical reformers use, and it refers to a loosely defined set of purportedly objectionable practices for finding associations in data. Often, “data-dredging” describes the practice of subjecting a single data set to many (sometimes hundreds or thousands of) statistical tests in an attempt to find a statistically significant result that is publishable.Critics of data-dredging allege that, because there is always a chance of finding a spurious association between two variables, one should be wary of “dredged” hypotheses. For severe testers, that default skepticism is justifiable: if an experiment is not designed to test a hypothesis, it is unlikely to be a severe test by chance. However, intuitions about data-dredging depend upon how the practice is described. Searching through data for patterns sounds exactly like what scientists should do!Mayo illustrates the dangers of data-dredging via several thought experiments. For example, she imagines a pharmaceutical company that hires scientists to find the benefits of a recently developed drug (267–68). When the hired researchers find no evidence that the drug is salutary in any of ways the manufacturer had hoped, they mine their data to find some salutary effect. Sure enough, they find the drug has an “impressive benefit on factor B” and begin marketing the drug with that promise. Mayo concludes that the hired scientists fail to subject the hypothesis to a severe test because they would have found an effect no matter what.Unfortunately, Mayo never explicitly applies her two criteria for severity to this example. Which hypothesis exactly is being tested? Here are two candidates. Let H 1 be the hypothesis “The drug has some salutary effect” and let H 2 be the hypothesis “The drug has the specific salutary effect found on B.” Even if the drug manufacturer is guaranteed to find some effect (i.e., H 1 is not severely tested), it does not follow that the manufacturer lacks evidence that the drug has the specific effect found by mining the data (i.e., it does not follow that H 2 is not severely tested). When I grade student logic exams, I am guaranteed to find that some student scored highest, but I do not thereby lack evidence that Roshan scored best when I tally her score and see it is highest. Similarly, cigarette manufacturers cannot avoid liability for causing lung cancer by (1) collecting enormous amounts of data, (2) investigating the relationship between cigarette smoking and hundreds of health problems, and then (3) claiming they were destined to find some statistically significant effect or another.2Some accusations of data-dredging, I claim, are instances of a fallacy that Mayo typically distinguishes for her readers with great care: the conflation of the pre-experimental reliability of a test with the postexperimental evidence provided by a particular data set.3 The history of science is replete with examples of unlikely, happy discoveries, where the data unequivocally support a hypothesis that scientists did not intend to test. In accessible, vivid prose, Mayo illustrates why one should typically be wary of data-dredging, but readers will be disappointed if they wish to learn how Mayo's two criteria can be used to distinguish between (a) hypotheses that are illicitly “dredged” and (b) hypotheses that are supported by one's data, despite being formulated postexperiment.Quibbles aside, Mayo's newest book is a yet another deep and engaging contribution to the philosophy of statistics and philosophy of science. Most importantly, it shows how foundational philosophical debates matter to pressing issues in science policy.

Eye tracking provides highly efficient, intuitive, and seamless human-machine interaction by measuring eye movements and revealing user attention and intention. Currently, video-based eye tracking systems, while achieving sub-degree accuracy, suffer from high power consumption due to data acquisition and processing of redundant pixels. Here, we present an energy-efficient eye-tracking system employing a position-sensitive detector (PSD) with a graphene photoconductive mesh, which operates with only four readout signals, reducing data volume by over three orders of magnitude compared to video-based systems, while achieving superior accuracy. With the synergistic effects of lateral photoelectric and interfacial gating, the mesh-structured PSD operates under eye-safe ultra-weak light (as low as 10pW) without a detection dead zone. Leveraging the real-time, precise tracking capability of the PSD, the eye-tracking system determines gaze orientation by tracking the reflected trajectories of light beams from the cornea, achieving a 1kHz tracking frequency and < 0.1° angular accuracy. Human-machine interaction applications such as eye-controlled typing and a Gluttonous Snake game are demonstrated using the PSD-enabled eye tracker. The PSD-based eye tracking holds significant promise for the application of human-machine interaction in miniaturized systems.

A fuzzy inference system (FIS) was developed to detect obstructive sleep apnea (OSA) by analyzing the respiratory airflow signal in adults. The parameters analyzed were the normalized area and the standard deviation of consecutive 3-second intervals of baseline adjusted and rectified airflow signal. Fuzzy logic was used to process these parameters to detect apnea and hypopnea when the output values were within a specified range extracted from OSA patient data. The FIS comprised of three major stages of computation: fuzzification, fuzzy rule evaluation and defuzzification. Seven males and two females with an average age of 48 years (range: 26 - 66 years), an average weight of 102 kg (range: 63 - 159 kg), an average height of 1.7 m (range: 1.5 - 1.8 m) and an average body mass index (BMI) of 33 kg/m/sup 2/ (range: 21 - 42 kg/m/sup 2/) participated in this study. Patients spent at least 8 hours in an accredited sleep laboratory. However, patient data was collected for only part of this time. The total amount of test time for all nine patients was 38.83 hours with an average of 4.31 hours/patient (range: 1.92 - 7.63 hours). The total number of apnea events occurring during this time was 808, and the number of hypopnea events was 694. The membership functions for the FIS were derived by analyzing apnea and hypopnea events in four patients. The data from all nine patients were used in algorithm performance evaluation. The apnea and hypopnea events were scored by a sleep specialist and were used to test the correct detection rate by the FIS. The results demonstrated that the FIS reached an overall correct detection rate of 83% across all patients. The false negative rate was 17% and the false positive rate was 12%. The correct detection rate varied from patient to patient and correct rates greater than 90% were achieved in three patients. This study suggests that fuzzy inference could provide an intelligent algorithm for control of a continuous positive airway pressure (CPAP) machine. It would detect apnea and hypopnea events and automatically adjust the pressure to eliminate them. The performance of the algorithm could be further optimized to give increased detection rates. This could be achieved by performing further studies on a larger OSA patient population and utilizing augmentative methods such as neural networks to better sense the fuzzy patterns in the OSA data.

This study compared the correct detection rates (CDR) of [¹⁸F]PSMA-1007 and [⁶⁸Ga]Ga-PSMA-11 positron emission tomography/computed tomography (PET/CT) in patients with prostate-specific antigen (PSA) ≤0.5 ng/mL after radical prostatectomy. This retrospective study included 88 patients who underwent [¹⁸F]PSMA-1007 (n=41) or [⁶⁸Ga]Ga-PSMA-11 PET/CT (n=47) for PSA ≤0.5 ng/mL after radical prostatectomy. Patients were stratified into ultra-low biochemical recurrence (u-BCR, <0.2 ng/mL) and early BCR (e-BCR, 0.2-0.5 ng/mL) groups. CDRs, defined as the proportion of patients with true positive findings for each imaging modality, and positive predictive values (PPVs) were compared between radiotracers. The overall CDR of [¹⁸F]PSMA-1007 was significantly higher than [⁶⁸Ga]Ga-PSMA-11 (70.7% vs. 23.4%, p<0.001) in both u-BCR (60.0% [9/15] vs. 19.0% [4/21], p=0.017) and e-BCR groups (76.9% [20/26] vs. 26.9% [7/26], p<0.001). Local recurrence was more frequently detected with [¹⁸F]PSMA-1007, particularly in the e-BCR group (46.2% vs. 3.8%, p<0.001), while lymph node (LN) and distant metastases detection was comparable. In the u-BCR group, the recurrence sites identified by [¹⁸F]PSMA-1007 included local (n=3), LN (n=3), bone (n=2), and local plus LN (n=1), whereas [⁶⁸Ga]Ga-PSMA-11 identified local (n=1), LN (n=1), bone (n=1), and lung (n=1). In [¹⁸F]PSMA-1007, CDR was higher in high/very high-risk than in low/intermediate-risk patients in the u-BCR group (p=0.011). No such difference was observed with [⁶⁸Ga]Ga-PSMA-11. PPVs did not differ significantly between radiotracers (p=0.333). [¹⁸F]PSMA-1007 PET/CT showed higher CDR than [⁶⁸Ga]Ga-PSMA-11 PET/CT in patients at PSA ≤0.5 ng/mL after radical prostatectomy. Given the high detection performance, these findings support consideration of [¹⁸F]PSMA-1007 PET/CT for detection at PSA ≤0.5 ng/mL, particularly for prostate bed assessment in high/very high-risk patients.

In this paper, Non-Line-of-Sight (NLoS) identification/mitigation is proposed, based on systematic statistical hypothesis testing. In the proposed method, the combined statistics of timing-based measurements and Received-Signal-Strength (RSS) are used, and the fact that all collected measurements are related to the same unknown position is exploited. Four implementations are proposed, ranging from the rigorous optimal approach to a much simplified and approximate approach, offering a trade-off between performance and complexity. Validation results, using actual Ultra-Wideband radio signals, show that the correct detection rate can go up to 99% and the corresponding positioning accuracy with NLoS mitigation is substantially improved: the Root Mean Squared Error (RMSE) of the estimated position is reduced by up to a factor of seven. The use of the simplest approach comes with only a minor decrease in performance, compared to the full optimal implementation. Copyright © 2013 Institute of Navigation

Accurate pupil center location plays an important role in eye tracking systems. This paper proposes a method for precise pupil location based on region projection. It is used to detect a center of pupil for extraction of point of eyeball in the face image. The proposed method has four stages. Firstly, the face image is detected and extracted. Secondly, minimizing location range is carried out in the already positioned face image so that the eye location is implemented in the future. Then, the region of the detection is narrowed down by applying the region projection. In the final stage, the precise center point of pupil is detected using the gravity method. Experimental results obtained by applying the proposed method on the human face image, demonstrate that the presented method has improved both the accuracy and speed of pupil location.