Title :
Multimedia multimodal methodologies
Author :
Guan, L. ; Muneesawang, P. ; Wang, Y. ; Zhang, R. ; Tie, Y. ; Bulzacki, A. ; Ibrahim, M.T.
Author_Institution :
Ryerson Multimedia Lab., Ryerson Univ., Toronto, ON, Canada
fDate :
June 28 2009-July 3 2009
Abstract :
This paper outlines several multimedia systems that utilize a multimodal approach. These systems include audiovisual based emotion recognition, image and video retrieval, and face and head tracking. Data collected from diverse sources/sensors are employed to improve the accuracy of correctly detecting, classifying, identifying, and tracking of a desired object or target. It is shown that the integration of multimodality data will be more efficient and potentially more accurate than if the data was acquired from a single source. A number of cutting-edge applications for multimodal systems will be discussed. An advanced assistance robot using the multimodal systems will be presented.
Keywords :
emotion recognition; face recognition; image classification; multimedia computing; object recognition; tracking; video retrieval; audiovisual based emotion recognition; cutting-edge application; diverse source/sensor; face recognition; head tracking; image retrieval; multimedia multimodal methodology; video retrieval; Data mining; Emotion recognition; Feature extraction; Fingerprint recognition; Fusion power generation; Multimedia systems; Noise level; Pattern recognition; Principal component analysis; Target tracking; Data fusion; multimedia; multimodal; pattern recognition;
Conference_Titel :
Multimedia and Expo, 2009. ICME 2009. IEEE International Conference on
Conference_Location :
New York, NY
Print_ISBN :
978-1-4244-4290-4
Electronic_ISBN :
1945-7871
DOI :
10.1109/ICME.2009.5202824