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.

Robust Discriminative Analysis Framework for Gaze and Headpose Estimation

Head movements, combined with gaze, play a fundamental role in predicting a person’s action and intention. In non-constrained head movement settings, the process is complex, and performance can degrade significantly in the presence of variation in head-pose, gaze position, occlusion and ambient illumination. In this thesis, a framework is therefore proposed to fuse and combine head-pose and gaze information to obtain more robust and accurate gaze estimation. Specific contributions include: the development of a newly developed graph-based model for pupil localization and accurate estimation of the pupil center; the proposal of a novel iris region descriptor feature using quadtree decomposition, that works together with pupil localization for gaze estimation; the proposal of kernel-based extensions and enhancements to a fusion mechanism known as Discriminative Multiple Canonical Correlation Analysis (DMCCA) for fusing features (proposed and traditional) together, to generate a refined, high quality feature set for classification; and the newly developed methodology of head-pose features based on quadtree decompositions and geometrical moments, to better integrate roll, yaw, pitch and jawline into the overall estimation framework. The experimental results of the proposed framework demonstrate robustness against variations in illumination, occlusion, head-pose and is calibration free. The proposed framework was validated on several datasets and scored: 4.5° using MPII, 4.4° using Cave, 4.8° using EYEDIAP, 5.0° using ACS, 4.1° using OSLO and 4.5° using UULM datasets respectively.

Robust Discriminative Analysis Framework for Gaze and Headpose Estimation

Head movements, combined with gaze, play a fundamental role in predicting a person’s action and intention. In non-constrained head movement settings, the process is complex, and performance can degrade significantly in the presence of variation in head-pose, gaze position, occlusion and ambient illumination. In this thesis, a framework is therefore proposed to fuse and combine head-pose and gaze information to obtain more robust and accurate gaze estimation. Specific contributions include: the development of a newly developed graph-based model for pupil localization and accurate estimation of the pupil center; the proposal of a novel iris region descriptor feature using quadtree decomposition, that works together with pupil localization for gaze estimation; the proposal of kernel-based extensions and enhancements to a fusion mechanism known as Discriminative Multiple Canonical Correlation Analysis (DMCCA) for fusing features (proposed and traditional) together, to generate a refined, high quality feature set for classification; and the newly developed methodology of head-pose features based on quadtree decompositions and geometrical moments, to better integrate roll, yaw, pitch and jawline into the overall estimation framework. The experimental results of the proposed framework demonstrate robustness against variations in illumination, occlusion, head-pose and is calibration free. The proposed framework was validated on several datasets and scored: 4.5° using MPII, 4.4° using Cave, 4.8° using EYEDIAP, 5.0° using ACS, 4.1° using OSLO and 4.5° using UULM datasets respectively.

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.