30 research outputs found
Low-cost Geometry-based Eye Gaze Detection using Facial Landmarks Generated through Deep Learning
Introduction: In the realm of human-computer interaction and behavioral
research, accurate real-time gaze estimation is critical. Traditional methods
often rely on expensive equipment or large datasets, which are impractical in
many scenarios. This paper introduces a novel, geometry-based approach to
address these challenges, utilizing consumer-grade hardware for broader
applicability. Methods: We leverage novel face landmark detection neural
networks capable of fast inference on consumer-grade chips to generate accurate
and stable 3D landmarks of the face and iris. From these, we derive a small set
of geometry-based descriptors, forming an 8-dimensional manifold representing
the eye and head movements. These descriptors are then used to formulate linear
equations for predicting eye-gaze direction. Results: Our approach demonstrates
the ability to predict gaze with an angular error of less than 1.9 degrees,
rivaling state-of-the-art systems while operating in real-time and requiring
negligible computational resources. Conclusion: The developed method marks a
significant step forward in gaze estimation technology, offering a highly
accurate, efficient, and accessible alternative to traditional systems. It
opens up new possibilities for real-time applications in diverse fields, from
gaming to psychological research
Appearance-Based Gaze Estimation in the Wild
Appearance-based gaze estimation is believed to work well in real-world
settings, but existing datasets have been collected under controlled laboratory
conditions and methods have been not evaluated across multiple datasets. In
this work we study appearance-based gaze estimation in the wild. We present the
MPIIGaze dataset that contains 213,659 images we collected from 15 participants
during natural everyday laptop use over more than three months. Our dataset is
significantly more variable than existing ones with respect to appearance and
illumination. We also present a method for in-the-wild appearance-based gaze
estimation using multimodal convolutional neural networks that significantly
outperforms state-of-the art methods in the most challenging cross-dataset
evaluation. We present an extensive evaluation of several state-of-the-art
image-based gaze estimation algorithms on three current datasets, including our
own. This evaluation provides clear insights and allows us to identify key
research challenges of gaze estimation in the wild
Robust eye tracking based on multiple corneal reflections for clinical applications
Postprint (published version
Eye Tracking Algorithm Based on Multi Model Kalman Filter
One of the most important pieces of Human Machine Interface (HMI) equipment is an eye tracking system that is used for many different applications. This paper aims to present an algorithm in order to improve the efficiency of eye tracking in the image by means of a multi-model Kalman filter. In the classical Kalman filter, one model is used for estimation of the object, but in the multi-model Kalman filter, several models are used for estimating the object. The important features of the multiple-model Kalman filter are improving the efficiency and reducing its estimating errors relative to the classical Kalman filter. The proposed algorithm consists of two parts. The first step is recognizing the initial position of the eye, and Support Vector Machine (SVM) has been used in this part. In the second part, the position of the eye is predicted in the next frame by using a multi-model Kalman filter, which applies constant speed and acceleration models based on the normal human eye. Doi: 10.28991/HIJ-2022-03-01-02 Full Text: PD
Distributed Real-Time Computation of the Point of Gaze
This paper presents a minimally intrusive real-time gaze-tracking prototype to be used in several scenarios, including a laboratory stall and an in-vehicle system. The system requires specific infrared illumination to allow it to work with variable light conditions. However, it is minimally intrusive due to the use of a carefully configured switched infrared LED array. Although the perceived level of illumination generated by this array is high, it is achieved using low-emission infrared light beams. Accuracy is achieved through a precise estimate of the center of the user's pupil. To overcome inherent time restrictions while using low-cost processors, its main image-processing algorithm has been distributed over four main computing tasks. This structure not only enables good performance, but also simplifies the task of experimenting with alternative computationally-complex algorithms and with alternative tracking models based on locating both user eyes and several cameras to improve user mobility