A theoretical eye model for uncalibrated real-time eye gaze estimation

Computer vision systems that monitor human activity can be utilized for many diverse applications. Some general applications stemming from such activity monitoring are surveillance, human-computer interfaces, aids for the handicapped, and virtual reality environments. For most of these applications, a non-intrusive system is desirable, either for reasons of covertness or comfort. Also desirable is generality across users, especially for humancomputer interfaces and surveillance. This thesis presents a method of gaze estimation that, without calibration, determines a relatively unconstrained user’s overall horizontal eye gaze. Utilizing anthropometric data and physiological models, a simple, yet general eye model is presented. The equations that describe the gaze angle of the eye in this model are presented. The procedure for choosing the proper features for gaze estimation is detailed and the algorithms utilized to find these points are described. Results from manual and automatic feature extraction are presented and analyzed. The error observed from this model is around 3± and the error observed from the implementation is around 6±. This amount of error is comparable to previous eye gaze estimation algorithms and it validates this model. The results presented across a set of subjects display consistency, which proves the generality of this model. A real-time implementation that operates around 17 frames per second displays the efficiency of the algorithms implemented. While there are many interesting directions for future work, the goals of this thesis were achieved

A Multi-Camera System for a Real-Time Pose Estimation

This paper presents a multi-camera system that performs face detection and pose estimation in real-time and may be used for intelligent computing within a visual sensor network for surveillance or humancomputer interaction. The system consists of a Scene View Camera (SVC), which operates at a fixed zoom level, and an Object View Camera (OVC), which continuously adjusts its zoom level to match objects of interest. The SVC is set to survey the whole filed of view. Once a region has been identified by the SVC as a potential object of interest, e.g. a face, the OVC zooms in to locate specific features. In this system, face candidate regions are selected based on skin color and face detection is accomplished using a Support Vector Machine classifier. The locations of the eyes and mouth are detected inside the face region using neural network feature detectors. Pose estimation is performed based on a geometrical model, where the head is modeled as a spherical object that rotates upon the vertical axis. The triangle formed by the mouth and eyes defines a vertical plane that intersects the head sphere. By projecting the eyes-mouth triangle onto a two dimensional viewing plane, equations were obtained that describe the change in its angles as the yaw pose angle increases. These equations are then combined and used for efficient pose estimation. The system achieves real-time performance for live video input. Testing results assessing system performance are presented for both still images and video