A multistage procedure to detect eye features is presented. Multiresolution and topographic classification are used to detect the iris center. The eye corner is calculated combining valley detection and eyelid curve extraction. The algorithm is tested in the BioID database and in a proprietary database containing more than 1200 images. The results show that the suggested algorithm is robust and accurate. Regarding the iris center our method obtains the best average behavior for the BioID database compared to other available algorithms. Additional contributions are that our algorithm functions in real time and does not require complex post processing stages. ACM Reference Format:Villanueva, A., Ponz, V., Sesma-Sanchez, L., Ariz, M., Porta, S., and Cabeza, R. 2013. Hybrid method based on topography for robust detection of iris center and eye corners. ACM Trans. Multimedia Comput. Commun.

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Remote eye tracking technology has suffered an increasing growth in recent years due to its applicability in many research areas. In this paper, a video-oculography method based on convolutional neural networks (CNNs) for pupil center detection over webcam images is proposed. As the first contribution of this work and in order to train the model, a pupil center manual labeling procedure of a facial landmark dataset has been performed. The model has been tested over both real and synthetic databases and outperforms state-of-the-art methods, achieving pupil center estimation errors below the size of a constricted pupil in more than 95% of the images, while reducing computing time by a 8 factor. Results show the importance of use high quality training data and well-known architectures to achieve an outstanding performance.

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Calibration is one of the most tedious and often annoying aspects of many eye tracking systems. It normally consists in looking at several marks on a screen in order to collect enough data to modify the parameters of an adjustable model. Unfortunately this step is unavoidable if a competent tracking system is desired. Many efforts have been made to achieve more competent and improved eye tracking systems. Maybe the search for an accurate mathematical model is one of the least researched fields. The lack of a parametric description of the gaze estimation problem makes it difficult to find the most suitable model, and therefore generic expressions in calibration and tracking sessions are employed instead. In other words, a model based on parameters describing the elements involved in the tracking system would provide a stronger basis and robustness. The aim of this work is to build up a mathematical model totally based in realistic variables describing elements taking part in an eye tracking system employing the well known bright pupil technique i.e. user, camera, illumination and screen. The model is said to be defined when the expression relating the point the user is looking at with the extracted features of the image (glint position and center of the pupil) is found. The desired model would have to be simple, realistic, accurate and easy to calibrate.The first step of the work consists in defining the parameters related to each element that present a faithful description of its behavior in the system. For the camera, the intrinsic parameters such as focal distance, aspect ratio and origin of the image plane can be easily found through a camera calibration process. The dimensions of the screen as well as the relative location and orientation related to a main reference system (located on the projection center of the camera) has to be measured. The user and illumination are due to be properly referenced to the same coordinate system. The mathematical description of the eyeball and the parametric definition of its behavior when looking at different points on the screen is one of the most problematic steps of the work. If an accurate description of the eye gaze is desired the differentiation between optical and visual axes of the eye is basic in addition to a deep knowledge of eye kinematics (Listing's and Donder's laws).Trying to perform a reasonable development of the model, in this work the description of the orientation of the optical axis of the eye is presented. This is the mathematical expression relating the optical axis orientation (p x ,p y ) with the information extracted from the captured image i.e. the glint-pupil vector.In order to make a practical development of the model the simplest configuration of the system is proposed, i.e. when the user, screen center and illumination are perfectly aligned on the optical axis of the camera. Once the elements have been properly described in a parametric way, useful geometric relations are established between them to achieve the pursued mathematical expression. ...

I2Head database has been created with the aim to become an optimal reference for low cost gaze estimation. It exhibits the following outstanding characteristics: it takes into account key aspects of low resolution eye tracking technology; it combines images of users gazing at different grids of points from alternative positions with registers of user's head position and it provides calibration information of the camera and a simple 3D head model for each user. Hardware used to build the database includes a 6D magnetic sensor and a webcam. A careful calibration method between the sensor and the camera has been developed to guarantee the accuracy of the data. Different sessions have been recorded for each user including not only static head scenarios but also controlled displacements and even free head movements. The database is an outstanding framework to test both gaze estimation algorithms and head pose estimation methods. CCS CONCEPTS • Information systems → Multimedia databases; • Humancentered computing → Human computer interaction (HCI);