IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Analysis of Facial Expressions: The State of the Art
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast Face Detection via Morphology-Based Pre-processing
ICIAP '97 Proceedings of the 9th International Conference on Image Analysis and Processing-Volume II
A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
Coding Facial Expressions with Gabor Wavelets
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Rule-based face detection in frontal views
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97) -Volume 4 - Volume 4
Detection of Anchor Points for 3D Face Veri.cation
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
Independent component analysis and support vector machine for face feature extraction
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
Hi-index | 0.00 |
Multiplayer online games (MOG) bring HCI into a new era of human-human interactions in computer world. Although current MOG provide more interactivity and social interaction in the virtual world, natural facial expression as a key factor in emulating face to face communications has been neglected by game designers. In this work, we propose a real-time automatic system to recognize players' facial expressions, so that the recognition results can be used to drive the MOG's "facial expression engine" instead of "text commands". Our major contributions are the evaluation, improvement and efficient implementation of existing algorithms to build a real-time system that meets the requirements specifically imposed by MOGs. In particular, we use a smaller number of fixed facial landmarks based on our evaluation to reduce the computational load with little degradation of the recognition performance.