Workshop on Perceptive User Interfaces 

Abstract: Head nods and head shakes are non-verbal gestures used often to communicate intent, emotion and to perform conversational functions. We describe a vision-based system that detects head nods and head shakes in real time and can act as a useful and basic interface to a machine. We use an infrared sensitive camera equipped with infrared LEDs to track pupils. The directions of head movements, determined using the position of pupils, are used as observations by a discrete Hidden Markov Model (HMM) based pattern analyzer to detect when a head nod/shake occurs. The system is trained and tested on natural data from ten users gathered in the presence of varied lighting and varied facial expressions. The system as described achieves a real time recognition accuracy of 78.46% on the test dataset.

Abstract: Freehand sketching is a natural and crucial part of everyday human interaction, yet is almost totally unsupported by current user interfaces. We are working to combine the exibility and ease of use of paper and pencil with the processing power of a computer, to produce a user interface for design that feels as natural as paper, yet is considerably smarter. One of the most basic steps in accomplishing this is converting the original digitized pen strokes in a sketch into the intended geometric objects. In this paper we describe an implemented system that combines multiple sources of knowledge to provide robust early processing for freehand sketching.

Abstract: 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 identi ed 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.

Abstract: Estimating a person's focus of attention is useful for various human-computer interaction applications, such as smart meeting rooms, where a user's goals and intent have to be monitored. In work presented here, we are interested in modeling focus of attention in a meeting situation. We have developed a system capable of estimating participants' focus of attention from multiple cues. We employ an omnidirectional camera to simultaneously track participants' faces around a meeting table and use neural networks to estimate their head poses. In addition, we use microphones to detect who is speaking. The system predicts participants' focus of attention from acoustic and visual information separately, and then combines the output of the audio- and video-based focus of attention predictors. We have evaluated the system using the data from three recorded meetings. The acoustic information has provided 8% error reduction on average compared to using a single modality.