10,001 research outputs found
Machine Analysis of Facial Expressions
No abstract
Dynamic Facial Expression of Emotion Made Easy
Facial emotion expression for virtual characters is used in a wide variety of
areas. Often, the primary reason to use emotion expression is not to study
emotion expression generation per se, but to use emotion expression in an
application or research project. What is then needed is an easy to use and
flexible, but also validated mechanism to do so. In this report we present such
a mechanism. It enables developers to build virtual characters with dynamic
affective facial expressions. The mechanism is based on Facial Action Coding.
It is easy to implement, and code is available for download. To show the
validity of the expressions generated with the mechanism we tested the
recognition accuracy for 6 basic emotions (joy, anger, sadness, surprise,
disgust, fear) and 4 blend emotions (enthusiastic, furious, frustrated, and
evil). Additionally we investigated the effect of VC distance (z-coordinate),
the effect of the VC's face morphology (male vs. female), the effect of a
lateral versus a frontal presentation of the expression, and the effect of
intensity of the expression. Participants (n=19, Western and Asian subjects)
rated the intensity of each expression for each condition (within subject
setup) in a non forced choice manner. All of the basic emotions were uniquely
perceived as such. Further, the blends and confusion details of basic emotions
are compatible with findings in psychology
A Mimetic Strategy to Engage Voluntary Physical Activity In Interactive Entertainment
We describe the design and implementation of a vision based interactive
entertainment system that makes use of both involuntary and voluntary control
paradigms. Unintentional input to the system from a potential viewer is used to
drive attention-getting output and encourage the transition to voluntary
interactive behaviour. The iMime system consists of a character animation
engine based on the interaction metaphor of a mime performer that simulates
non-verbal communication strategies, without spoken dialogue, to capture and
hold the attention of a viewer. The system was developed in the context of a
project studying care of dementia sufferers. Care for a dementia sufferer can
place unreasonable demands on the time and attentional resources of their
caregivers or family members. Our study contributes to the eventual development
of a system aimed at providing relief to dementia caregivers, while at the same
time serving as a source of pleasant interactive entertainment for viewers. The
work reported here is also aimed at a more general study of the design of
interactive entertainment systems involving a mixture of voluntary and
involuntary control.Comment: 6 pages, 7 figures, ECAG08 worksho
Automated drowsiness detection for improved driving safety
Several approaches were proposed for the detection and prediction of drowsiness. The approaches can be categorized as estimating the fitness of duty, modeling the sleep-wake rhythms, measuring the vehicle based performance and online operator monitoring. Computer vision based online operator monitoring approach has become prominent due to its predictive ability of detecting drowsiness. Previous studies with this approach detect driver drowsiness primarily by making preassumptions about the relevant behavior, focusing on blink rate, eye closure, and yawning. Here we employ machine learning to datamine actual human behavior during drowsiness episodes. Automatic classifiers
for 30 facial actions from the Facial Action Coding system were developed
using machine learning on a separate database of spontaneous expressions. These facial actions include blinking and yawn motions, as well as a number of other facial movements. In addition, head motion was collected through automatic eye tracking and an accelerometer. These measures were passed to learning-based classifiers such as Adaboost and multinomial ridge regression. The system was able to predict sleep and crash episodes during a driving computer game with 96% accuracy within subjects and above 90% accuracy across subjects. This is the highest prediction rate reported to date for detecting real drowsiness. Moreover, the analysis revealed new information about human behavior during drowsy drivin
- …