Abstract. The human ability to express and recognize emotions plays an impor-tant role in face-to-face communication, and as technology advances it will be in-creasingly important for computer-generated avatars to be similarly expressive. In this paper, we present the detailed development process for the Lifelike Responsive Avatar Framework (LRAF) and a prototype application for modeling a specific in-dividual to analyze the eectiveness of expressive avatars. In particular, the goals of our pilot study (n 1,744) are to determine whether the specific avatar being developed is capable of conveying emotional states (Ekmans six classic emotions) via facial features and whether a realistic avatar is an appropriate vehicle for con-veying the emotional states accompanying spoken information. The results of this study show that happiness and sadness are correctly identified with a high degree of accuracy while the other four emotional states show mixed results. Fig. 1. Dr. Alex Schwarzkopf and His Virtual Representation 1

-Frequency analysis to detect and characterize the periodic motion. The periodicity is also analyzed robustly using the 2D lattice structures inherent in similarity matrices. A real-time system has been implemented to track and classify objects using periodicity. Examples of object classification (people, running

Attitudes toward risk were measured in 240 households using two methods: an interview method eliciting certainty equivalents and an experimental gambling approach with real payoffs which, at their maximum, exceeded monthly incomes of unskilled laborers. The interview method is subject

gloves for input. We achieve high recognition rates for full sentence ASL using only visual cues. A forty word lexicon consisting of personal pronouns, verbs, nouns, and adjectives is used to create 494 randomly constructed five word sentences that are signed by the subject to the computer. The data

tasks using the same architecture. In addition, we quantify how the representation affects detection performance by considering several alternate representations including pixels and principal components. We also describe a real-time application of our person detection system as part of a driver

's perimeter with that of another module focusing on the color histogram of the ellipse's interior. Since these two modules have roughly orthogonal failure modes, they serve to complement one another. The result is a robust, real-time system that is able to track a person's head with enough accuracy

Camera shake during exposure leads to objectionable image blur and ruins many photographs. Conventional blind deconvolution methods typically assume frequency-domain constraints on images, or overly simplified parametric forms for the motion path during camera shake. Real camera motions can follow

as separate processes --- background subtraction is followed by blob detection and tracking --- which prevents a principled computation of image likelihoods. This paper presents two theoretical advances which address this limitation and lead to a robust multiple-person tracking system suitable for single

in intelligent environments, we created a practical person-tracking system that solves most of the real-world problems. It uses two sets of color stereo cameras for tracking multiple people during live demonstrations in a living room. The stereo images are used for locating people, and the color images are used

, are required to undergo an experience, such as being exposed to loud noises or watching a film clip. They are asked to provide a continuous indication of the hedonic quality of their experience in real time by manipulating a lever that controls a marker on a scale, which is usually defined by extreme values