Recognition of affect in the wild using deep neural networks

Kollias, Dimitrios, Nicolaou, Mihalis A., Kotsia, Irene, Zhao, Guoying and Zafeiriou, Stefanos (2017) Recognition of affect in the wild using deep neural networks. In: CVPRW 2017: 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 21-26 July 2017, Honolulu, USA.

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Abstract

In this paper we utilize the first large-scale "in-the-wild" (Aff-Wild) database, which is annotated in terms of the valence-arousal dimensions, to train and test an end-to-end deep neural architecture for the estimation of continuous emotion dimensions based on visual cues. The proposed architecture is based on jointly training convolutional (CNN) and recurrent neural network (RNN) layers, thus exploiting both the invariant properties of convolutional features, while also modelling temporal dynamics that arise in human behaviour via the recurrent layers. Various pre-trained networks are used as starting structures which are subsequently appropriately fine-tuned to the Aff-Wild database. Obtained results show premise for the utilization of deep architectures for the visual analysis of human behaviour in terms of continuous emotion dimensions and analysis of different types of affect.

Item Type: Conference or Workshop Item (Lecture)
Additional Information: Published as: D. Kollias, M. A. Nicolaou, I. Kotsia, G. Zhao and S. Zafeiriou, "Recognition of Affect in the Wild Using Deep Neural Networks," 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Honolulu, HI, 2017, pp. 1972-1979. doi: 10.1109/CVPRW.2017.247
Research Areas: A. > School of Science and Technology > Computer Science
Item ID: 22046
Notes on copyright: © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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Depositing User: Irene Kotsia
Date Deposited: 16 Jun 2017 16:42
Last Modified: 04 Apr 2019 05:02
URI: https://eprints.mdx.ac.uk/id/eprint/22046

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