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In head-mounted eye tracking systems, the correct detection of pupil position is a key factor in estimating gaze direction. However, this is a challenging issue when the videos are recorded in real-world conditions, due to the many sources of noise and artifacts that exist in these scenarios, such as rapid changes in illumination, reflections, occlusions and an elliptical appearance of the pupil. Thus, it is an indispensable prerequisite that a pupil detection algorithm is robust in these challenging conditions. In this work, we present one pupil center detection method based on searching the maximum contribution point to the radial symmetry of the image. Additionally, two different center refinement steps were incorporated with the aim of adapting the algorithm to images with highly elliptical pupil appearances. The performance of the proposed algorithm is evaluated using a dataset consisting of 225,569 head-mounted annotated eye images from publicly available sources. The results are compared with the better algorithm found in the bibliography, with our algorithm being shown as superior. [ABSTRACT FROM AUTHOR]

Active shape models (ASMs) are some of the most actively used model-based segmentation approaches, whose usefulness has been successfully demonstrated in a wide variety of applications. However, they suffer from two important drawbacks: (1) the large number of training samples required to adequately model the object of interest and (2) their dependence on the initialization. Partial solutions for both of these problems have been proposed but come at the expense of a significant increase in computation time, which is of great importance in current applications. Based on the wavelet transform, the present paper proposes a new full multi-resolution framework that allows not only to hierarchically decompose any type of shape into a set of bands of information, but also to create instances of the shape with different degrees of detail. This is a multi-resolution decomposition of the shape space. Combining this new approach with a multi-resolution Gaussian pyramid of the image, the result is a new full multi-resolution hierarchical ASM that reduces both the size of the training set required and the initialization dependence while improving the classical segmentation algorithms accuracy and computational complexity. The advantages of this new algorithm have been successfully verified over two different databases. [ABSTRACT FROM AUTHOR]

The incorporation of a multiresolution image approach is one of the most popular variants of Active Shape Models (ASMs), providing a more robust algorithm and minimizing its initialization dependency. Using the wavelet transform, the present paper extends the multiresolution analysis to the shape space, developing a novel multiresolution shape framework, capable of being incorporated into most of ASM variants. The tests performed with two different types of images, face images (AR database) and chest radiographs (JSRT database), demonstrate how this new generation of algorithms significantly reduce the computational cost, more than halving it, while maintaining the same levels of accuracy. [ABSTRACT FROM AUTHOR]

This paper presents a principled analysis of various combinations of image features to determine their suitability for remote eye tracking. It begins by reviewing the basic theory underlying the connection between eye image and gaze direction. Then a set of approaches is proposed based on different combinations of well-known features and their behaviour is evaluated, taking into account various additional criteria such as free head movement, and minimum hardware and calibration requirements. The paper proposes a final method based on multiple glints and the pupil centre; the method is evaluated experimentally. Future trends in eye tracking technology are also discussed. [ABSTRACT FROM AUTHOR]

One of the most confusing aspects that one meets when introducing oneself into gaze tracking technology is the wide variety, in terms of hardware equipment, of available systems that provide solutions to the same matter, that is, determining the point the subject is looking at. The calibration process permits generally adjusting nonintrusive trackers based on quite different hardware and image features to the subject. The negative aspect of this simple procedure is that it permits the system to work properly but at the expense of a lack of control over the intrinsic behavior of the tracker. The objective of the presented article is to overcome this obstacle to explore more deeply the elements of a video-oculographic system, that is, eye, camera, lighting, and so forth, from a purely mathematical and geometrical point of view. The main contribution is to find out the minimum number of hardware elements and image features that are needed to determine the point the subject is looking at. A model has been constructed based on pupil contour and multiple lighting, and successfully tested with real subjects. On the other hand, theoretical aspects of video-oculographic systems have been thoroughly reviewed in order to build a theoretical basis for further studies. [ABSTRACT FROM AUTHOR]