Paper Info
Title
Transfer Of Multimodal Emotion Features In Deep Belief Networks
Abstract
In this paper, we investigate the effect of transfer of emotion-rich features between source and target networks on classification accuracy and training time in a multimodal setting for vision based emotion recognition. First, we propose emosource-a 6-layer Deep Belief Network (DBN), trained on popular emotion corpora for emotion classification. Second, we propose two 6-layer DBNs-emotarget and emotarget(ft) and study the transfer of emotion features between source and target networks in a layer-by-layer fashion. To the best of our knowledge, this is the first research effort to study the transfer of emotion features layer-bylayer in a multimodal setting.
Year
Venue
Field
2016
2016 50TH ASILOMAR CONFERENCE ON SIGNALS, SYSTEMS AND COMPUTERS
Emotion recognition,Computer science,Deep belief network,Emotion classification,Electronic engineering,Robustness (computer science),Speech recognition,Vision based,Artificial intelligence,Ubiquitous computing,Market research
DocType
ISSN
Citations 
Conference
1058-6393
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Hiranmayi Ranganathan1141.57
Shayok Chakraborty213717.47
Sethuraman Panchanathan31431152.04