This work explores a new direction in utilizing eye gaze for computer input. Gaze tracking has long been considered as an alternative or potentially superior pointing method for computer input. We believe that many fundamental limitations exist with traditional gaze pointing. In particular, it is unnatural to overload a perceptual channel such as vision with a motor control task. We therefore propose an alternative approach, dubbed MAGIC (Manual And Gaze Input Cascaded) pointing. With such an approach, pointing appears to the user to be a manual task, used for fine manipulation and selection. However, a large portion of the cursor movement is eliminated by warping the cursor to the eye gaze area, which encompasses the target. Two specific MAGIC pointing techniques, one conservative and one liberal, were designed, analyzed, and implemented with an eye tracker we developed. They were then tested in a pilot study. This earlystage exploration showed that the MAGIC pointing techniques might offer many advantages, including reduced physical effort and fatigue as compared to traditional manual pointing, greater accuracy and naturalness than traditional gaze pointing, and possibly faster speed than manual pointing. The pros and cons of the two techniques are discussed in light of both performance data and subjective reports.

Reliable detection and tracking of eyes is an important requirement for attentive user interfaces. In this paper, we present a methodology for detecting eyes robustly in indoor environments in real-time. We exploit the physiological properties and appearance of eyes as well as head/eye motion dynamics. Infrared lighting is used to capture the physiological properties of eyes, Kalman trackers are used to model eye/head dynamics, and a probabilistic based appearance model is used to represent eye appearance. By combining three separate modalities, with specific enhancements within each modality, our approach allows eyes to be treated as robust features that can be used for other higher-level processing. 1. Introduction In this paper, we present methods for tracking and detecting eyes in indoor environments. Robust and reliable eye detection is an important first step towards the development of user interfaces capable of gaze tracking and detecting eye contact. To support concurrent hig...

Abstract not found

In the eye gaze tracking problem, the goal is to determine where on a monitor screen a computer user is looking – the gaze point. Existing systems generally have one of two limitations: either the head must remain fixed in front of a stationary camera, or, to allow for head motion, the user must wear an obtrusive device. We introduce a 3D eye tracking system where head motion is allowed without the need for markers or worn devices. We use a pair of stereo systems: a wide angle stereo system detects the face and steers an active narrow FOV stereo system to track the eye at high resolution. For high resolution tracking, the eye is modeled in 3D, including the corneal ball, pupil and fovea. In this paper, we discuss the calibration of the stereo systems, the eye model, eye detection and tracking, and we close with an evaluation of the accuracy of the estimated gaze point on the monitor. 1

Robust, real-time, fully automatic tracking of facial features is required for many computer vision and graphics applications. In this paper, we describe a fully automatic system that tracks eyes and eyebrows in real time. The pupils are tracked using the red eye effect by an infrared sensitive camera equipped with infrared LEDs. Templates are used to parameterize the facial features. For each new frame, the pupil coordinates are used to extract cropped images of eyes and eyebrows. The template parameters are recovered by PCA analysis on these extracted images using a PCA basis, which was constructed during the training phase with some example images. The system runs at 30 fps and requires no manual initialization or calibration. The system is shown to work well on sequences with considerable head motions and occlusions.

This paper provides a new fully automatic framework to analyze facial action units, the fundamental building blocks of facial expression enumerated in Paul Ekman’s Facial Action Coding System (FACS). The action units examined in this paper include upper facial muscle movements such as inner eyebrow raise, eye widening, and so forth, which combine to form facial expressions. Although prior methods have obtained high recognition rates for recognizing facial action units, these methods either use manually preprocessed image sequences or require human specification of facial features; thus, they have exploited substantial human intervention. This paper presents a fully automatic method, requiring no such human specification. The system first robustly detects the pupils using an infrared sensitive camera equipped with infrared LEDs. For each frame, the pupil positions are used to localize and normalize eye and eyebrow regions, which are analyzed using PCA to recover parameters that relate to the shape of the facial features. These parameters are used as input to classifiers based on Support Vector Machines to recognize upper facial action units and all their possible combinations. On a completely natural dataset with lots of head movements, pose changes and occlusions, the new framework achieved a recognition accuracy of 69.3 % for each individual AU and an accuracy of 62.5 % for all possible AU combinations. This framework achieves a higher recognition accuracy on the Cohn-Kanade AU-coded facial expression database, which has been previously used to evaluate other facial action recognition system. 1

We present the design and implementation of a perceptual user interface for a responsive dialog-box agent that employs real-time computer vision to recognize user acknowledgements from head gestures (e.g., nod = yes). IBM Pupil-Cam technology together with anthropometric head and face measures are used to first detect the location of the user’s face. Salient facial features are then identified and tracked to compute the global 2-D motion direction of the head. For recognition, timings of natural gesture motion are incorporated into a state-space model. The interface is presented in the context of an enhanced text editor employing a perceptual dialog-box agent. 1.

Shoulder-surfing – using direct observation techniques, such as looking over someone's shoulder, to get passwords, PINs and other sensitive personal information – is a problem that has been difficult to overcome. When a user enters information using a keyboard, mouse, touch screen or any traditional input device, a malicious observer may be able to acquire the user’s password credentials. We present EyePassword, a system that mitigates the issues of shoulder surfing via a novel approach to user input. With EyePassword, a user enters sensitive input (password, PIN, etc.) by selecting from an on-screen keyboard using only the orientation of their pupils (i.e. the position of their gaze on screen), making eavesdropping by a malicious observer largely impractical. We present a number of design choices and discuss their effect on usability and security. We conducted user studies to evaluate the speed, accuracy and user acceptance of our approach. Our results demonstrate that gaze-based password entry requires marginal additional time over using a keyboard, error rates are similar to those of using a keyboard and subjects preferred the gaze-based password entry approach over traditional methods.

Non-intrusive methods based on active remote IR illumination for eye tracking is important for many applications of vision-based man-machine interaction. One problem that has plagued those methods is their sensitivity to lighting condition change. This tends to significantly limit their scope of application. In this paper, we present a new real-time eye detection and tracking methodology that works under variable and realistic lighting conditions. Based on combining the bright-pupil effect resulted from IR light and the conventional appearance-based object recognition technique, our method can robustly track eyes when the pupils are not very bright due to significant external illumination interferences. The appearance model is incorporated in both eyes detection and tracking via the use of support vector machine and the mean shift tracking. Additional improvement is achieved from modifying the image acquisition apparatus including the illuminator and the camera.

We present EyePliances: appliances and devices that detect and respond to human visual attention using eye contact sensors. EyePliances receive implicit input [3] from users, in the form of eye gaze, and respond by opening communication channels. By allowing devices to recognize the attentional cues people already provide, requests for explicit input from users can be reduced. Further, eye contact sensing gives devices a mechanism to determine whether a user is available for interruption, and can provide the missing environmental context to improve speech recognition.