Deep impression: Audiovisual deep residual networks for multimodal apparent personality trait recognition

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Title:	Deep impression: Audiovisual deep residual networks for multimodal apparent personality trait recognition
Author(s):	Güçlütürk, Y. ; Güçlü, U. ; Gerven, M.A.J. van ; Lier, R.J. van
Publication year:	2016
In:	Hua, G.; Jégou, H. (ed.), Computer Vision - ECCV 2016 Workshops, Proceedings, pp. 349-358
Publisher:	Cham : Springer
ISBN:	9783319494081
Annotation:	Computer Vision - ECCV 2016 Workshops, Amsterdam, The Netherlands, October 8-10 and 15-16, 2016
Publication type:	Article in monograph or in proceedings
Please use this identifier to cite or link to this item : https://hdl.handle.net/2066/200738
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Editor(s):	Hua, G.; Jégou, H.
Subject:	Action, intention, and motor control Cognitive artificial intelligence DI-BCB_DCC_Theme 2: Perception, Action and Control DI-BCB_DCC_Theme 4: Brain Networks and Neuronal Communication
Organization:	SW OZ DCC CO SW OZ DCC KI
Book title:	Hua, G.; Jégou, H. (ed.), Computer Vision - ECCV 2016 Workshops, Proceedings
Page start:	p. 349
Page end:	p. 358
Abstract:	Here, we develop an audiovisual deep residual network for multimodal apparent personality trait recognition. The network is trained end-to-end for predicting the Big Five personality traits of people from their videos. That is, the network does not require any feature engineering or visual analysis such as face detection, face landmark alignment or facial expression recognition. Recently, the network won the third place in the ChaLearn First Impressions Challenge with a test accuracy of 0.9109.
Languages used:	English (eng)