Transfer of multimodal emotion features in deep belief networks

Hiranmayi Ranganathan; Shayok Chakraborty; Sethuraman Panchanathan

doi:10.1109/ACSSC.2016.7869079

Transfer of multimodal emotion features in deep belief networks

Hiranmayi Ranganathan, Shayok Chakraborty, Sethuraman Panchanathan

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

5 Scopus citations

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 emotargetft 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-by-layer in a multimodal setting.

Original language	English (US)
Title of host publication	Conference Record of the 50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016
Publisher	IEEE Computer Society
Pages	449-453
Number of pages	5
ISBN (Electronic)	9781538639542
DOIs	https://doi.org/10.1109/ACSSC.2016.7869079
State	Published - Mar 1 2017
Event	50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016 - Pacific Grove, United States Duration: Nov 6 2016 → Nov 9 2016

Other

Other	50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016
Country/Territory	United States
City	Pacific Grove
Period	11/6/16 → 11/9/16

ASJC Scopus subject areas

Signal Processing
Computer Networks and Communications

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10.1109/ACSSC.2016.7869079

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Transfer of multimodal emotion features in deep belief networks. / Ranganathan, Hiranmayi; Chakraborty, Shayok; Panchanathan, Sethuraman.
Conference Record of the 50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016. IEEE Computer Society, 2017. p. 449-453 7869079.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Ranganathan, H, Chakraborty, S & Panchanathan, S 2017, Transfer of multimodal emotion features in deep belief networks. in Conference Record of the 50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016., 7869079, IEEE Computer Society, pp. 449-453, 50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016, Pacific Grove, United States, 11/6/16. https://doi.org/10.1109/ACSSC.2016.7869079

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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 emotargetft 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-by-layer in a multimodal setting.",

author = "Hiranmayi Ranganathan and Shayok Chakraborty and Sethuraman Panchanathan",

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AB - 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 emotargetft 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-by-layer in a multimodal setting.

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ER -

Transfer of multimodal emotion features in deep belief networks

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