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@article{dobrivsek2013towards,

title = {Towards efficient multi-modal emotion recognition},

author = {Simon Dobrišek and Rok Gajšek and France Mihelič and Nikola Pavešić and Vitomir Štruc},

url = {https://lmi.fe.uni-lj.si/en/towardsefficientmulti-modalemotionrecognition/},

doi = {10.5772/54002},

year  = {2013},

date = {2013-01-01},

urldate = {2013-01-01},

journal = {International Journal of Advanced Robotic Systems},

volume = {10},

number = {53},

abstract = {The paper presents a multi-modal emotion recognition system exploiting audio and video (i.e., facial expression) information. The system first processes both sources of information individually to produce corresponding matching scores and then combines the computed matching scores to obtain a classification decision. For the video part of the system, a novel approach to emotion recognition, relying on image-set matching, is developed. The proposed approach avoids the need for detecting and tracking specific facial landmarks throughout the given video sequence, which represents a common source of error in video-based emotion recognition systems, and, therefore, adds robustness to the video processing chain. The audio part of the system, on the other hand, relies on utterance-specific Gaussian Mixture Models (GMMs) adapted from a Universal Background Model (UBM) via the maximum a posteriori probability (MAP) estimation. It improves upon the standard UBM-MAP procedure by exploiting gender information when building the utterance-specific GMMs, thus ensuring enhanced emotion recognition performance. Both the uni-modal parts as well as the combined system are assessed on the challenging multi-modal eNTERFACE'05 corpus with highly encouraging results. The developed system represents a feasible solution to emotion recognition that can easily be integrated into various systems, such as humanoid robots, smart surveillance systems and alike.},

keywords = {avid database, emotion recognition, facial expression recognition, multi modality, speech technologies},

pubstate = {published},

tppubtype = {article}

}