In this paper, we focus on the facial expression translation task and propose a novel Expression Conditional GAN (ECGAN) which can learn the mapping from one image domain to another one based on an additional expression attribute. The proposed ECGAN is a generic framework and is applicable to different expression generation tasks where specific facial expression can be easily controlled by the conditional attribute label. Besides, we introduce a novel face mask loss to reduce the influence of background changing. Moreover, we propose an entire framework for facial expression generation and recognition in the wild, which consists of two modules, i.e., generation and recognition. Finally, we evaluate our framework on several public face datasets in which the subjects have different races, illumination, occlusion, pose, color, content and background conditions. Even though these datasets are very diverse, both the qualitative and quantitative results demonstrate that our approach is able to generate facial expressions accurately and robustly.

Expression Conditional Gan for Facial Expression-to-Expression Translation / Tang, H.; Wang, W.; Wu, S.; Chen, X.; Xu, D.; Sebe, N.; Yan, Yan. - 2019-:(2019), pp. 4449-4453. ((Intervento presentato al convegno 26th IEEE International Conference on Image Processing, ICIP 2019 tenutosi a Taipei International Convention Center (TICC), twn nel September 22-25, 2019 [10.1109/ICIP.2019.8803654].

Expression Conditional Gan for Facial Expression-to-Expression Translation

Tang H.;Wang W.;Xu D.;Sebe N.;Yan Y.
2019

Abstract

In this paper, we focus on the facial expression translation task and propose a novel Expression Conditional GAN (ECGAN) which can learn the mapping from one image domain to another one based on an additional expression attribute. The proposed ECGAN is a generic framework and is applicable to different expression generation tasks where specific facial expression can be easily controlled by the conditional attribute label. Besides, we introduce a novel face mask loss to reduce the influence of background changing. Moreover, we propose an entire framework for facial expression generation and recognition in the wild, which consists of two modules, i.e., generation and recognition. Finally, we evaluate our framework on several public face datasets in which the subjects have different races, illumination, occlusion, pose, color, content and background conditions. Even though these datasets are very diverse, both the qualitative and quantitative results demonstrate that our approach is able to generate facial expressions accurately and robustly.
Proceedings - International Conference on Image Processing, ICIP
New York
IEEE Computer Society
978-1-5386-6249-6
Tang, H.; Wang, W.; Wu, S.; Chen, X.; Xu, D.; Sebe, N.; Yan, Yan
Expression Conditional Gan for Facial Expression-to-Expression Translation / Tang, H.; Wang, W.; Wu, S.; Chen, X.; Xu, D.; Sebe, N.; Yan, Yan. - 2019-:(2019), pp. 4449-4453. ((Intervento presentato al convegno 26th IEEE International Conference on Image Processing, ICIP 2019 tenutosi a Taipei International Convention Center (TICC), twn nel September 22-25, 2019 [10.1109/ICIP.2019.8803654].
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11572/250823
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