A Review and Analysis of Eye-Gaze Estimation Systems, Algorithms and Performance Evaluation Methods in Consumer Platforms

In this paper a review is presented of the research on eye gaze estimation techniques and applications, that has progressed in diverse ways over the past two decades. Several generic eye gaze use-cases are identified: desktop, TV, head-mounted, automotive and handheld devices. Analysis of the literature leads to the identification of several platform specific factors that influence gaze tracking accuracy. A key outcome from this review is the realization of a need to develop standardized methodologies for performance evaluation of gaze tracking systems and achieve consistency in their specification and comparative evaluation. To address this need, the concept of a methodological framework for practical evaluation of different gaze tracking systems is proposed.Comment: 25 pages, 13 figures, Accepted for publication in IEEE Access in July 201

Effects of search intent on eye-movement patterns in a change detection task

The goal of the present study was to examine whether intention type affects eye movement patterns in a change detection task In addition, we assessed whether the eye movement index could be used to identify human implicit intent. We attempted to generate three types of intent amongst the study participants, dividing them into one of three conditions; each condition received different information regarding an impending change to the visual stimuli. In the “navigational intent” condition, participants were asked to look for any interesting objects, and were not given any more information about the impending change. In the “low-specific intent” condition, participants were informed that a change would occur. In the “high-specific intent” condition, participants were told that a change would occur, and that an object would disappear. In addition to this main change detection task, participants also had to perform a primary task, in which they were required to name aloud the colors of objects in the pre-change scene. This allowed us to control for the visual searching process during the pre-change scene. The main results were as follows: firstly, the primary task successfully controlled for the visual search process during the pre-change scene, establishing that there were no differences in the patterns of eye movements across all three conditions despite differing intents. Secondly, we observed significantly different patterns of eye movement between the conditions in the post-change scene, suggesting that generating a specific intent for change detection yields a distinctive pattern of eye-movements. Finally, discriminant function analysis showed a reasonable classification rate for identifying a specific intent. Taken together, it was found that both participant intent and the specificity of information provided to the participants affect eye movements in a change detection task

Project Iris: Image reconstruction of the iris spectrally

The basic motivation behind this project is a highly accurate representation of the human iris, to be injected into a virtual model of the human eyeball. A highly accurate brightness level recording can be easily obtained with a high quality digital camera. Color, however, is an entirely different matter. Photography in the traditional sense entertains all sorts of color inaccuracies, mostly related to the chemical process of development. Digital photography presents gamma and metameric problems, since the exact conditions of the capturing event cannot easily be duplicated. However, the spectral radiance of an object can be captured, utilizing a spectrophotometer and reliable light source. In this research, a priori measurements and analysis of the human iris spectral reflectances are performed. Using a spectroradiometer spectral reflectance samples from human iris are taken and this sample set is analyzed using principal component analysis to give a number of basis functions to reconstruct the original reflectance with sufficient accuracy. A color transformation can be built between the signals from a photometric linear digital camera and the weight coefficients of the eigenvectors. Finally, the spectral reflectance can be derived from the digital counts of the camera giving us a highly accurate representation of a human iris

Geometric Generative Gaze Estimation (G3E) for Remote RGB-D Cameras

We propose a head pose invariant gaze estimation model for distant RGB-D cameras. It relies on a geometric understanding of the 3D gaze action and generation of eye images. By introducing a semantic segmentation of the eye region within a generative process, the model (i) avoids the critical feature tracking of geometrical approaches requiring high resolution images; (ii) decouples the person dependent geometry from the ambient conditions, allowing adaptation to different conditions without retraining. Priors in the generative framework are adequate for training from few samples. In addition, the model is capable of gaze extrapolation allowing for less restrictive training schemes. Comparisons with state of the art methods validate these properties which make our method highly valuable for addressing many diverse tasks in sociology, HRI and HCI

Multistream Gaze Estimation with Anatomical Eye Region Isolation by Synthetic to Real Transfer Learning

We propose a novel neural pipeline, MSGazeNet, that learns gaze representations by taking advantage of the eye anatomy information through a multistream framework. Our proposed solution comprises two components, first a network for isolating anatomical eye regions, and a second network for multistream gaze estimation. The eye region isolation is performed with a U-Net style network which we train using a synthetic dataset that contains eye region masks for the visible eyeball and the iris region. The synthetic dataset used in this stage is procured using the UnityEyes simulator, and consists of 80,000 eye images. Successive to training, the eye region isolation network is then transferred to the real domain for generating masks for the real-world eye images. In order to successfully make the transfer, we exploit domain randomization in the training process, which allows for the synthetic images to benefit from a larger variance with the help of augmentations that resemble artifacts. The generated eye region masks along with the raw eye images are then used together as a multistream input to our gaze estimation network, which consists of wide residual blocks. The output embeddings from these encoders are fused in the channel dimension before feeding into the gaze regression layers. We evaluate our framework on three gaze estimation datasets and achieve strong performances. Our method surpasses the state-of-the-art by 7.57% and 1.85% on two datasets, and obtains competitive results on the other. We also study the robustness of our method with respect to the noise in the data and demonstrate that our model is less sensitive to noisy data. Lastly, we perform a variety of experiments including ablation studies to evaluate the contribution of different components and design choices in our solution.Comment: 15 pages, 7 figures, 14 tables. This work has been accepted to the IEEE Transactions on Artificial Intelligence $\copyright$ 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other use

Eye Tracking: A Perceptual Interface for Content Based Image Retrieval

In this thesis visual search experiments are devised to explore the feasibility of an eye gaze driven search mechanism. The thesis first explores gaze behaviour on images possessing different levels of saliency. Eye behaviour was predominantly attracted by salient locations, but appears to also require frequent reference to non-salient background regions which indicated that information from scan paths might prove useful for image search. The thesis then specifically investigates the benefits of eye tracking as an image retrieval interface in terms of speed relative to selection by mouse, and in terms of the efficiency of eye tracking mechanisms in the task of retrieving target images. Results are analysed using ANOVA and significant findings are discussed. Results show that eye selection was faster than a computer mouse and experience gained during visual tasks carried out using a mouse would benefit users if they were subsequently transferred to an eye tracking system. Results on the image retrieval experiments show that users are able to navigate to a target image within a database confirming the feasibility of an eye gaze driven search mechanism. Additional histogram analysis of the fixations, saccades and pupil diameters in the human eye movement data revealed a new method of extracting intentions from gaze behaviour for image search, of which the user was not aware and promises even quicker search performances. The research has two implications for Content Based Image Retrieval: (i) improvements in query formulation for visual search and (ii) new methods for visual search using attentional weighting. Futhermore it was demonstrated that users are able to find target images at sufficient speeds indicating that pre-attentive activity is playing a role in visual search. A current review of eye tracking technology, current applications, visual perception research, and models of visual attention is discussed. A review of the potential of the technology for commercial exploitation is also presented