Paper Info
Title
Bimodal information analysis for emotion recognition
Abstract
We present a bimodal information analysis system for automatic emotion recognition. Our approach is based on the analysis of video sequences which combines facial expressions observed visually with acoustic features to automatically recognize five universal emotion classes: anger, disgust, happiness, sadness and surprise. We address the challenges posed during the temporal analysis of the bimodal data and introduce a novel technique for combining the best features of instantaneous and temporal based visual recognition systems. We obtain robust appearance-based visual features which we classify instantaneously and aggregate it temporally to improve the recognition rates when compared to single-frame based instantaneous classification. The performance of the system is further boosted by using the complementary audio information for the bimodal emotion recognition. We combine the two modalities at both feature and score level to compare the respective joint emotion recognition rates. The emotions are instantaneously classified using a support vector machine and sequentially aggregated based on their classification probabilities. This approach is validated on a posed audio-visual database and a natural interactive database. The experiments performed on these databases provide encouraging results with the best combined recognition rate being 82%.
Year
DOI
Venue
2009
10.1109/WACV.2009.5403035
WACV
Keywords
Field
DocType
video signal processing,automatic emotion recognition,instantaneous classification,face recognition,video sequences,bimodal data,visual recognition systems,audio-visual database,acoustic features,visual databases,interactive database,bimodal emotion recognition,complementary audio information,emotion recognition,acoustic signal processing,image classification,support vector machine,bimodal information analysis,image sequences,classification probability,audio databases,universal emotion classes,recognition rates,support vector machines,facial expressions,probability,temporal analysis,hidden markov models,databases,facial expression,feature extraction,visualization,information analysis
Computer science,Artificial intelligence,Contextual image classification,Computer vision,Facial recognition system,Sadness,Pattern recognition,Visualization,Support vector machine,Feature extraction,Speech recognition,Facial expression,Hidden Markov model
Conference
ISSN
ISBN
Citations 
1550-5790
978-1-4244-5497-6
7
PageRank 
References 
Authors
0.53
22
3
Name
Order
Citations
PageRank
Malika Meghjani1355.66
Frank P Ferrie272988.57
Gregory Dudek32163255.48