Visual field test system for digital display equipment

Abstract

The present system is intended to enable self-service field-of-view testing at home using common computing devices and without the participation of experts. The system can adapt to the differences of different technical platforms, and according to various described embodiments, the accuracy and reliability of detection are ensured by calibrating the screen size and brightness, monitoring the real-time viewing distance, adjusting the display curvature, and using a front-facing camera to identify the staring stability and detect the eye shielding condition.

Description

Technical Field

[0001] This invention is a system designed to assess and monitor visual field thresholds using common digital display devices, thereby achieving accurate detection and even self-detection. The system aims to replace traditional dedicated bowl-shaped visual field testing equipment while utilizing various digital display consumer electronics devices to provide accurate visual field testing and progress monitoring. Background of the Invention

[0002] Glaucoma is one of the leading causes of blindness worldwide, characterized by degeneration of the optic nerve, often associated with optic disc cupping and elevated intraocular pressure (IOP). Glaucoma typically begins to impair vision in the peripheral areas. Therefore, the standard diagnostic method for glaucoma is visual field (VF) testing, which covers a wide area (e.g., 30 degrees). This type of test, called a visual field test or automated visual field test, uses a machine with a bowl-shaped surface to project light spots of varying intensities. The patient focuses their attention on the central point and responds to the peripheral light spots; the machine then measures the sensitivity of the retina and generates a graphical representation of the deviation from normal values, thus aiding in the diagnosis and monitoring of glaucoma.

[0003] Alternative eye testing methods based on digital displays have been proposed, including US Patent US20230165460A1 (Shousha, June 1, 2023). This method uses wearable devices to display stimuli at various locations in the field of vision and utilizes spatial information to select appropriate display locations. It also collects feedback based on eye responses through sensors to assist in the diagnosis of eye abnormalities.

[0004] US Patent US20130155376A1 (Huang et al., June 20, 2013) relates to a video game designed to map a subject's peripheral vision. The game includes a moving visual fixation point identified by the subject's actions and requires the subject to locate briefly presented visual stimuli. The game runs on a platform including a video display, user input devices, and a video camera to map the subject's visual perception thresholds and compare them with age-stratified standard data.

[0005] However, there is an urgent need for a vision testing method that can be performed at home, without professional guidance, and operated using conventional computing devices. However, variations in monitor characteristics, such as screen resolution and brightness, as well as improper use and changes in environmental factors, can lead to relatively low accuracy in vision testing on these devices. Summary of the Invention

[0006] The described field-of-view testing system is designed for self-service field-of-view testing at home using common digital display devices. In this embodiment, the system employs calibration steps to adapt to different screen sizes and brightness levels, and ensures testing accuracy through multiple methods, including maintaining a stable viewing distance, detecting improper use, and taking into account the curvature of the digital display.

[0007] Similar to standard visual field tests, subjects are required to fixate on a fixed point and respond to stimuli displayed in the peripheral location.

[0008] This system allows users to adjust the distance between calibration markers and offset adjustment controls to match the size of a fixed object, thus adapting to differences in screen sizes. The system can then calculate the screen size scaling factor based on this distance and adjust the display ratio accordingly. This process can be accurately completed by non-professional home users using standard computer equipment.

[0009] To address the instability of screen brightness, the system may first display a first stimulus at a brightness below a known visual threshold (i.e., the lowest contrast level that the human eye can just detect), then display a second stimulus at a brightness slightly above the threshold, and calculate the screen brightness based on the frequency of responses to these stimuli. The system determines the screen brightness by analyzing the number of responses received for each stimulus.

[0010] The system ensures a proper viewing distance by calibrating it. This calibration involves setting a digital display at a specific distance from the user's face and establishing reference calibration facial feature parameters by analyzing image data from the camera. Real-time facial feature parameters are determined through continuous analysis of image data and compared to the calibration parameters to determine the real-time viewing distance, thus monitoring the viewing distance. If the viewing distance deviates from the calibrated distance, the system will issue a warning to the user.

[0011] For smaller screens, the system can adjust the position of the fixed viewpoint to the four corners of the screen to achieve a test viewing distance farther than the center point. The system can also apply a scaling factor to adjust the spot size to reduce threshold differences, thereby addressing the tangent effect problem of digital displays, especially in peripheral positions.

[0012] The system can capture eye images using the front-facing camera and analyze the pixel intensity of key features to monitor gaze stability, thereby ensuring reliable visual field testing. The system can also verify that untested eyes are correctly occluded by capturing eye images and ensure that the correct eye is occluded; if the wrong eye is occluded, the system will issue a warning message.

[0013] In this embodiment, the system converts the position under the planar screen geometry to the position under the curved screen geometry, and detects the background ambient brightness by analyzing the overall and local brightness in the image data acquired from the camera.

[0014] For visual field testing, the system employs a Bayesian probabilistic prediction method, using a probability density function (PDF) encompassing both normal and affected eyes to perform a binary search for threshold contrast at each location. This allows for rapid and accurate estimation of the threshold based on the subject's response matrix. Neighborhood logic is used to check for errors in the response and recover from potential damage to the threshold logic, while the initial seed strength prediction for each test location is optimized based on endpoints of neighboring locations. Furthermore, the system can adjust the speed of the testing "waiting window" based on the average response speed of each individual subject to stimuli, achieving adaptive response. If the subject responds quickly, the software shortens the interval between stimuli; if the response is slow, the interval is increased. The algorithm dynamically adjusts the time based on the running average of past responses.

[0015] Overall, this system enables convenient and efficient field-of-view testing on commonly used devices, providing accurate results, while also solving the technical challenges involved in transitioning from curved, bowl-shaped surfaces to digital displays.

[0016] Other aspects of the invention are also disclosed. Attached Figure Description

[0017] While the invention may cover any other forms, preferred embodiments of this disclosure are described here by way of example only with reference to the accompanying drawings, in which: Figure 1 The key components and functions of the visual field testing system are shown, including a personal computing device with a digital display used to display the visual field testing user interface; Figure 2 The processing flow performed by the screen size calibration controller is shown; Figures 3A to 3C The screen size calibration user interface presented by the screen size calibration controller is shown, including a fixed viewpoint and a stimulus that scales according to a calculated screen size scaling factor. Figure 4 The diagram illustrates the processing flow performed by the screen brightness calibration controller, showing three scenarios: calibrated screen brightness detection, excessive brightness detection, and excessively low brightness detection. Figure 5 Three types of screen brightness detection are described: calibrated screen brightness detection, excessive brightness detection, and excessive dimness detection; Figure 6The processing flow executed by the gaze stability detection controller is shown, which detects gaze stability by analyzing eye image data; Figure 7 The operation of the gaze stability detection controller is shown, which evaluates gaze stability by detecting image properties of the eye region; Figure 8 The processing flow executed by the eye occlusion detection controller is shown; Figure 9 The operation of the eye occlusion detection controller is shown, which detects occluded and unoccluded eye areas in facial image data; Figure 10 The processing flow performed by the viewing distance calibration controller is displayed; Figures 11A to 11D An exemplary image processing flow performed by the viewing distance calibration controller is shown, including face width detection and boundary analysis; Figure 12 This illustrates the mapping from planar screen geometry to curved screen geometry; and Figure 13 The process flow for bright spot detection is shown. Detailed Implementation

[0018] Figure 1Some key components 101 and functions 102 of a visual field testing system 100 are shown. This system includes a personal computing device (such as a tablet PC) with a digital display 103 on which a visual field testing user interface is displayed. According to a preferred embodiment, the system 100 employs a web server architecture, wherein the personal computing device is operatively communicating with a web server via the Internet, and the personal computing device is equipped with a web browser application 104 for sending HTTP requests to the web server and rendering web pages provided by the web server in response to the HTTP requests. The personal computing device includes a processor for processing digital data, which is operatively communicating with a storage device for storing digital data including computer program code instructions. In use, the processor retrieves these computer program code instructions and related data from the storage device for interpreting and performing the functions described herein. The computer program code instructions may be logically divided into multiple computer program code instruction controllers 105, which will be further detailed below. The computer program code instruction controllers may include a stimulus testing controller for assessing visual field function 106 by displaying fixation points and peripheral stimuli on the digital display 103 and recording responses to the peripheral stimuli. In an embodiment, system 100 may include a front-facing camera 107, wherein image data acquired by camera 107 is subjected to computer vision analysis 108 for various uses, which will be further detailed below. Controller 105 may include a screen size calibration controller 109 that calculates a screen size scaling factor to ensure that the actual distance between the stimulus and the fixation point remains consistent across different types of digital displays, regardless of screen size and / or resolution.

[0019] Figure 2 The processing flow 110 performed by the screen size calibration controller is shown. Figure 3CThe screen size calibration user interface displayed by the controller is shown. In step 111, the controller 109 displays calibration marks 115 (calibration lines in this example) and offset adjustment controls 116 (decreasing and increasing buttons in this example), which can be controlled to adjust the distance 117 between the marks 115. The user is instructed to place a fixed-size object (such as a ruler 118) between the marks 115 and use the offset adjustment controls 116 to increase or decrease the display distance 117 between the marks 115 until the distance between the marks 115 matches the set length (e.g., 5 cm) of the ruler 118. The system 100 calculates a screen size scaling factor based on the distance between the marks 115 and then scales the screen size according to the screen size scaling factor, thereby ensuring that the actual distance between the stimulus and the fixed viewpoint remains consistent regardless of how the screen size and / or screen resolution of the different types of digital displays 103 change. In step 113, the controller 109 can convert the distance between the marks 115 into the number of pixels per unit length to account for the impact of screen resolution.

[0020] Figure 3C A visual field testing user interface 119 is shown, equipped with a fixation point 120 and stimuli 121, which are scaled from a small size 122 to a large size 123 based on a calculated screen size scaling factor. It should be noted that the scaling factor can be calibrated separately on the X and Y axes of the digital display 103 to accommodate differences in screen resolution along the X and Y axes. See also... Figure 1 The controller 105 may also include a screen brightness calibration controller 124 for calibrating the brightness of the digital display 103 to accommodate any screen brightness variations that may exist between different types of personal computing devices. The screen brightness calibration controller 124 may display instructions to increase or decrease the screen brightness, or it may adjust the screen brightness automatically.

[0021] Figure 4 The processing flow 125 executed by the screen brightness calibration controller 124 is shown, thereby obtaining calibrated screen brightness detection 126, excessive brightness detection 127, and insufficient brightness detection 128. Specifically, the screen brightness calibration controller 124 is used to display a darker stimulus 129A at brightness levels below a threshold and a brighter stimulus 129B at brightness levels above a threshold. The stimulus 129 can be represented as adjacent grayscale dots displayed on the screen. In step 130, the user needs to react according to the visibility of stimuli 129A and 129B. For example, if both stimuli 129A and 129B are visible, the user will react to both; if only the brighter stimulus 129B is visible, the user will only react to 129B; if neither stimulus 129 is visible, no reaction will be made.

[0022] Figure 4 The diagram shows a high-frequency response 131A (i.e., intensity greater than 50%) to a darker stimulus 129A and a low-frequency response 131B (i.e., intensity less than 50%) to a brighter stimulus 129B. Figure 5 As shown, when the two responses 131A and 131B are equal, a calibrated screen brightness 126 is detected. When a high-frequency response 131A is detected to a lower-brightness stimulus 129A, excessively high screen brightness 127 is detected, at which point the user can be instructed to reduce the screen brightness. When a low-frequency response 131B is detected to a brighter stimulus 129B, insufficient (too dim) screen brightness 128 is detected. Similarly, the user can be instructed to increase the screen brightness.

[0023] refer to Figure 1 The controller 105 may further include a viewing distance controller 133 for calibrating the viewing distance by analyzing image data acquired from the camera to determine reference calibration facial feature parameters, with the digital display 103 positioned at a distance from the user's face. The user may be instructed to use an object of fixed length (such as a 30 cm ruler) to place the digital display 103 in the appropriate position. During use, the viewing distance controller 133 can monitor the real-time viewing distance: by continuously monitoring image data acquired from the camera to determine real-time facial feature parameters and comparing the calibration reference facial feature parameters with the real-time facial feature parameters, the real-time viewing distance is determined. If the display 103 is too close or too far, the viewing distance controller 133 can issue a warning to the user. The viewing distance controller 133 can calculate calibration facial feature parameters in pixel widths, which may represent face width, eye distance, nose width, or jawline width. Furthermore, the viewing distance controller 133 can determine the calibration facial feature parameters using at least one of boundary analysis, contrast difference analysis, and color difference analysis.

[0024] Figure 10 The processing flow 135 executed by the viewing distance calibration controller 133 is shown. Figures 11A to 11D An exemplary image processing flow performed by the viewing distance calibration controller 133 is illustrated. This processing can be personalized based on each user's facial features, allowing the viewing distance controller 133 to adapt to small or large faces. In step 137, the viewing distance controller 133 captures an image 143 of the user's face, such as... Figure 11A As shown, and in step 138, the image undergoes computer vision analysis 108. Computer vision analysis 108 may include detecting the face width 144, such as... Figure 11BAs shown. The face width 144 can be calculated in pixels. The face width 144 may include the detection of the width of the visually detected face boundary 145. The boundary 145 can be determined through boundary detection analysis, which includes intensity analysis and color difference analysis.

[0025] Intensity difference analysis detects edges by detecting changes in pixel intensity. For example, in a facial image, the boundary 145 between the face and the background typically manifests as a significant change in brightness. By analyzing these intensity gradients, the viewing distance controller 133 can effectively delineate the facial boundary 145. Color difference analysis evaluates the color differences between adjacent pixels. In a facial image, skin tone may contrast with the background or other facial features, such as eyes or hair. By evaluating these color changes, the viewing distance controller 133 can accurately identify the edges of the face.

[0026] Computer vision analysis 108 can employ edge detection algorithms, such as the Canny edge detector. This method uses a multi-stage processing flow, including gradient calculation, non-maximum suppression, and hysteresis edge tracking, to generate an accurate edge map of the image. Furthermore, machine learning methods can be used to enhance the boundary detection of the viewing distance controller 133. A convolutional neural network (CNN) can be trained to recognize and delineate boundaries through centralized learning on labeled datasets.

[0027] In an alternative embodiment, computer vision analysis 108 is used to detect the width between the outer edges of the eyes. Computer vision analysis 108 may utilize Haar cascades to detect the interocular width in a facial image. Haar cascades are pre-trained classifiers that detect specific facial features based on edge or line detection. This approach involves using a series of stages, each containing a set of Haar-like features for identifying eyes in an image. Once the eyes are detected, the viewing distance controller 133 measures the pixel distance between their centers.

[0028] Computer vision analysis108 can also employ convolutional neural networks (CNNs) specifically trained for facial landmark detection to accurately locate key facial features, such as the corners of the eyes. For example, a facial landmark detection model can identify the coordinates of the inner and outer corners of each eye, enabling precise calculation of eye distance. This approach benefits from the robustness and accuracy of deep learning models, especially when trained on large datasets of labeled facial images.

[0029] Computer vision analysis 108 can also use the Histogram of Oriented Gradients (HOG) feature descriptor to detect eyes. This descriptor focuses on the structure of local gradients, which can represent edges and textures. By extracting HOG features from facial images and applying a sliding window technique, the viewing distance controller 133 can detect the presence and location of eyes. Once identified, the width between the detected eye regions can be calculated.

[0030] In step 139, the viewing distance controller 133 may be calibrated, wherein the user is instructed to hold the digital display 103 at a certain distance from the face, such as 30 cm. During this calibration process, the viewing distance controller 133 calculates a reference width 144 representing the calibration distance; subsequently, in step 140, the real-time viewing distance is calculated based on the real-time width 144 and the calibration reference width 144. In step 141, the viewing distance controller 133 determines whether the actual viewing distance exceeds the reference distance by a threshold (e.g., exceeding 10%); if it does, in step 142, the viewing distance controller 133 may display a warning to the user, informing the user to move the digital display 103 closer or further away, depending on the situation. Figure 11C This illustrates a scenario where the user is too far from the digital display 103, resulting in the visually detected facial boundary 145 being smaller than the calibration reference width 144; and Figure 11D This illustrates a situation where the user is too close to the digital display 103, causing the visually detected facial boundary to be wider than the calibrated width 144.

[0031] In this embodiment, the stimulus testing controller is used to convert the eccentricity and angular position of stimulus 121 into equivalent Cartesian XY coordinate values ​​on the digital display 103. Furthermore, the stimulus testing controller can be used to apply a scaling factor to the display of stimulus 121 to address the visible tangent effect problem that occurs when the stimulus is displayed on the digital display. In other words, stimulus 121 can be displayed larger and with elliptical deformation, with the degree of deformation increasing the further away from the fixed viewpoint 120.

[0032] Reference Figure 1 The controller 105 may also include a gaze stability detection controller 146 for detecting gaze stability by analyzing eye image data acquired by the camera 107. Figure 6 The processing flow 134 of the gaze stability detection controller 146 is shown. Figure 7 An exemplary operation is shown. In step 148, the gaze stability detection controller 146 performs gaze detection on the eye image data of the user's eye region 152 captured in step 149. In step 150, the gaze stability detection controller 146 detects gaze stability; if gaze instability is detected, a gaze instability warning message is displayed to the user in step 151.

[0033] Figure 7 A gaze stability detection controller 146 is shown that can be used to detect image attributes 153 within an eye region 152. These image attributes 153 typically represent variations in image brightness, with larger brightness variations detectable at the edges of the iris and / or pupil. Alternatively, image attributes 153 can also be color attributes. As shown, image attributes 153 may include image attribute 153A detected on the X-axis of the eye region 152 and image attribute 153B detected on the Y-axis of the eye region 152.

[0034] Figure 7 The image shows a change in gaze in the horizontal direction, where image attribute 153A changes along the X-axis. The gaze stability detection controller 146 can also detect changes in gaze in the vertical direction using image attribute 153B along the Y-axis of the eye region 152.

[0035] Reference Figure 1 The controller 105 may also include an eye occlusion detection controller 154 for detecting eye occlusion by analyzing facial image data 143 acquired from the camera 107. The system 100 may employ the eye occlusion detection controller 154 to ensure that any untested eye is closed or occluded. Figure 8 The processing flow 156 of the eye occlusion detection controller 154 is shown. Figure 9 An exemplary operation is shown. (Refer to...) Figure 9 The facial image data 143 may have an occluded eye region 152A (in this embodiment, this region is occluded by an eye mask) and an unoccluded eye region 152B. In step 157, the eye occlusion detection controller 154 captures the eye region 152 from the facial image data 143 captured by the camera 107. In step 158, image processing is performed on the captured eye region; in step 159, the eye occlusion detection controller 154 detects the occluded eye. Detecting the occluded eye may include detecting the color of the eye mask, identifying whether eye features are missing, or whether the pixels in the detection area are highly uniform due to the lack of eye features. In other embodiments, detecting the occluded eye may include: eye recognition, identifying only one eye in the facial image data 143. In step 159, the eye occlusion detection controller 154 detects whether the eye being detected is occluded; if so, a warning message is displayed to the user in step 160.

[0036] In this embodiment, the controller may include a screen geometry transformation controller, as referenced. Figure 12This controller maps the display of stimulus 121 from a planar digital screen to a curved digital screen, specifically focusing on mapping point A on the planar screen to point B on the curved screen. In this setup, the observer is located at point C, with a viewing distance denoted as d, which is the distance from point C to the planar screen. The curved screen is represented as an arc with radius R, originating at the center of curvature. The diagram shows two main planes: the plane containing the planar digital screen (on which point A lies) and the plane containing the curved digital screen (on which point B lies). The mapping process involves calculating new coordinates on the curved screen based on the viewing geometry. The line of sight emanating from point C intersects the planar screen at point A and the curved screen at point B. The equation y = -d / L1x + d represents the line of sight, while x^2 + (yR)^2 = R^2 describes the curved screen. Solving these equations simultaneously yields the coordinates of point B on the curved screen.

[0037] In one embodiment, the controller may include a bright spot detection controller for implementing... Figure 13 The process 170 shown begins in step 171 where the front-facing camera scans an image of the background scene. Then, in step 172, system 100 checks if any pixels have a brightness exceeding a predetermined threshold level. If such pixels are detected, system 100 identifies the starting point of a bright pixel cluster in step 173. Then, in step 174, the pixel cluster is expanded by including adjacent pixels exceeding the brightness threshold until the entire pixel cluster is identified in step 175. Then, at decision point 176, system 100 evaluates whether the pixel cluster exceeds a specified size. If the pixel cluster is larger than the specified size, it is considered a bright spot in step 177, and a warning message is displayed to the user. If the pixel cluster does not exceed the specified size, the system continues scanning in step 178 to look for other potential bright spots.

[0038] For purposes of explanation, the foregoing description uses specific terminology to provide a comprehensive understanding of the invention. However, it will be apparent to those skilled in the art that no specific information is required to practice the invention. Therefore, the foregoing description of specific embodiments is for illustrative and descriptive purposes and is not intended to be exhaustive or to limit the invention to the precise forms disclosed. Many modifications and variations can be made given the foregoing details. The selected and described embodiments are intended to better explain the principles of the invention and its practical application, thereby enabling others skilled in the art to best utilize the invention and its various embodiments, and to make various modifications suitable for a particular intended use. The following claims and their equivalents are intended to define the scope of the invention.

Claims

1. A visual field testing system, comprising a digital display for recording responses to stimuli displayed on the digital display, wherein, The system is used to scale screen size, including: Display calibration markers and offset adjustment controls, the controls being used to adjust the distance between the calibration markers; Calculate the screen size scaling factor based on the distance; and The screen size is scaled according to the screen size scaling factor.

2. The system according to claim 1, wherein, The system is used to calculate the number of pixels per unit length of the screen based on the distance.

3. The system according to claim 2, wherein, The system is used to calculate the screen size scaling factors of the digital display along the X-axis and Y-axis, and to scale the screen size along the X-axis and Y-axis according to the screen size scaling factors, respectively.

4. The system according to claim 1, wherein, The system is used to calibrate screen brightness, including: Display a first stimulus with a brightness below a visual threshold, and a second stimulus with a brightness above the threshold; and The screen brightness is calculated based on the response to the stimulus.

5. The system according to claim 4, wherein, The screen brightness is categorized as calibrated, insufficient, and excessive.

6. The system according to claim 4, wherein, The screen brightness is determined to be too high by the frequency of response to the first stimulus exceeding a threshold.

7. The system according to claim 4, wherein, Insufficient screen brightness is determined by the frequency of response to the second stimulus being lower than a threshold.

8. The system according to claim 4, wherein, The screen brightness is determined to be calibrated by ensuring that the response frequencies to the two stimuli are approximately equal.

9. The system according to claim 1, wherein, The system also includes a camera, and the system is used for: Calibrate viewing distance, including: The digital display is placed at a certain distance from the user's face; Analyze the image data acquired from the camera to determine calibrated facial feature parameters; Monitoring real-time viewing distance, including: Continuously monitor image data acquired from the camera to determine real-time facial feature parameters; The calibrated facial feature parameters are compared with the real-time facial feature parameters to determine the real-time viewing distance.

10. The system according to claim 9, wherein, The calibrated facial feature parameters are measured in pixel width.

11. The system according to claim 9, wherein, The calibrated facial feature parameters include at least one of face width, distance between eyes, nose width, and jawline width.

12. The system according to claim 11, wherein, The system is used to determine calibrated facial feature parameters by employing at least one of boundary analysis, contrast difference analysis, and color difference analysis.

13. The system according to claim 1, wherein, The stimulus test controller is used to adjust the position of the fixed viewpoint display on the digital display.

14. The system according to claim 13, wherein, The stimulus test controller is used to adjust the fixed viewpoint display position according to the size of the digital display.

15. The system according to claim 14, wherein, The stimulus test controller is used to set the fixation point display position in the periphery or corner area of ​​the digital display.

16. The system according to claim 1, wherein, The system is used to display a fixed viewpoint and scale the geometry of the stimulus proportionally based on the display distance between the stimulus and the fixed viewpoint.

17. The system according to claim 16, wherein, The stimulus exhibits an elliptical geometry.

18. The system according to claim 17, wherein, When the stimulus is displayed, the greater the degree of elliptic distortion, the farther away from the fixed viewpoint.

19. The system according to claim 1, wherein, The system also includes a camera, and is further configured to identify stable gaze conditions by analyzing user eye image data acquired from the camera.

20. The system according to claim 19, wherein, The system is used to detect image attributes of the eye region in at least one direction of the X-axis and Y-axis.

21. The system according to claim 20, wherein, The image attribute is at least one of brightness level, contrast level, and color value.

22. The system according to claim 20, wherein, The system is used to detect changes in the image properties of the eye region on the X-axis to identify situations of gaze instability.

23. The system according to claim 22, wherein, The system is used to detect changes in the image properties of the eye region on the Y-axis to identify situations of gaze instability.

24. The system according to claim 1, wherein, The system also includes a camera, and is further configured to detect eye occlusion by analyzing facial image data acquired from the camera.

25. The system according to claim 24, wherein, The system employs eye image recognition and identifies cases where only one eye is present.

26. The system according to claim 1, wherein, The system is used to convert the stimulus position under the geometry of a planar screen into the stimulus position under the geometry of a curved screen.

27. The system according to claim 26, wherein, The system is used to calculate the intersection of the line of sight between the geometry of a planar screen and the geometry of a curved screen.

28. The system according to claim 1, wherein, The system also includes a front-facing camera, and the system is further used to detect bright spots in image data acquired from the camera.

29. The system according to claim 28, wherein, The system is used for: Scan the image of the background scene; Determine if any pixel's brightness exceeds a predetermined threshold; If a bright pixel is detected, the starting position of the bright pixel cluster is identified; The cluster is expanded by including adjacent pixels that exceed a brightness threshold; Determine whether the cluster exceeds the specified size.

30. The system according to claim 29, wherein, The system is further configured to display a warning message if the cluster exceeds a specified size.

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