Imperial College London
Alternate

STEFANOS ZAFEIRIOU, PhD

Faculty of Engineering, Department of Computing

Professor in Machine Learning & Computer Vision
 
 
 
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Contact

 

+44 (0)20 7594 8461s.zafeiriou Website CV

 
 
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Location

 

375Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Tzirakis:2017:10.1109/JSTSP.2017.2764438,
author = {Tzirakis, P and Trigeorgis, G and Nicolaou, MA and Schuller, BW and Zafeiriou, S},
doi = {10.1109/JSTSP.2017.2764438},
journal = {IEEE Journal of Selected Topics in Signal Processing},
pages = {1301--1309},
title = {End-to-end multimodal emotion recognition using deep neural networks},
url = {http://dx.doi.org/10.1109/JSTSP.2017.2764438},
volume = {11},
year = {2017}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Automatic affect recognition is a challenging task due to the various modalities emotions can be expressed with. Applications can be found in many domains including multimedia retrieval and human-computer interaction. In recent years, deep neural networks have been used with great success in determining emotional states. Inspired by this success, we propose an emotion recognition system using auditory and visual modalities. To capture the emotional content for various styles of speaking, robust features need to be extracted. To this purpose, we utilize a convolutional neural network (CNN) to extract features from the speech, while for the visual modality a deep residual network of 50 layers is used. In addition to the importance of feature extraction, a machine learning algorithm needs also to be insensitive to outliers while being able to model the context. To tackle this problem, long short-term memory networks are utilized. The system is then trained in an end-to-end fashion where-by also taking advantage of the correlations of each of the streams-we manage to significantly outperform, in terms of concordance correlation coefficient, traditional approaches based on auditory and visual handcrafted features for the prediction of spontaneous and natural emotions on the RECOLA database of the AVEC 2016 research challenge on emotion recognition.
AU  - Tzirakis,P
AU  - Trigeorgis,G
AU  - Nicolaou,MA
AU  - Schuller,BW
AU  - Zafeiriou,S
DO  - 10.1109/JSTSP.2017.2764438
EP  - 1309
PY  - 2017///
SN  - 1932-4553
SP  - 1301
TI  - End-to-end multimodal emotion recognition using deep neural networks
T2  - IEEE Journal of Selected Topics in Signal Processing
UR  - http://dx.doi.org/10.1109/JSTSP.2017.2764438
UR  - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000416226000007&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - http://hdl.handle.net/10044/1/52827
VL  - 11
ER  -
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