A Theoretical Study on Six Classifier Fusion Strategies
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Evaluation of Grading Classifiers
IDA '01 Proceedings of the 4th International Conference on Advances in Intelligent Data Analysis
Experiments with Classifier Combining Rules
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Bimodal HCI-related affect recognition
Proceedings of the 6th international conference on Multimodal interfaces
A Theoretical and Experimental Analysis of Linear Combiners for Multiple Classifier Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Issues in stacked generalization
Journal of Artificial Intelligence Research
Face detection and tracking in video sequences using the modifiedcensus transformation
Image and Vision Computing
Evaluation and Discussion of Multi-modal Emotion Recognition
ICCEE '09 Proceedings of the 2009 Second International Conference on Computer and Electrical Engineering - Volume 01
DaFEx: database of facial expressions
INTETAIN'05 Proceedings of the First international conference on Intelligent Technologies for Interactive Entertainment
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Switching between selection and fusion in combining classifiers: anexperiment
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Recently, automatic emotion recognition has been established as a major research topic in the area of human computer interaction (HCI). Since humans express emotions through various channels, a user's emotional state can naturally be perceived by combining emotional cues derived from all available modalities. Yet most effort has been put into single-channel emotion recognition, while only a few studies with focus on the fusion of multiple channels have been published. Even though most of these studies apply rather simple fusion strategies -- such as the sum or product rule -- some of the reported results show promising improvements compared to the single channels. Such results encourage investigations if there is further potential for enhancement if more sophisticated methods are incorporated. Therefore we apply a wide variety of possible fusion techniques such as feature fusion, decision level combination rules, meta-classification or hybrid-fusion. We carry out a systematic comparison of a total of 16 fusion methods on different corpora and compare results using a novel visualization technique. We find that multi-modal fusion is in almost any case at least on par with single channel classification, though homogeneous results within corpora point to interchangeability between concrete fusion schemes.