dc.contributor.author | Jyoti, S. | |
dc.contributor.author | Sharma, G. | |
dc.contributor.author | Dhall, A. | |
dc.date.accessioned | 2019-08-24T11:37:39Z | |
dc.date.available | 2019-08-24T11:37:39Z | |
dc.date.issued | 2019-08-24 | |
dc.identifier.uri | http://localhost:8080/xmlui/handle/123456789/1360 | |
dc.description.abstract | The paper explores the topic of Facial Action Unit (FAU) detection in the wild. In particular, we are interested in answering the following questions: (1) How useful are residual connections across dense blocks for face analysis? (2) How useful is the information from a network trained for categorical Facial Expression Recognition (FER) for the task of FAU detection? The proposed network (ResiDen) exploits dense blocks along with residual connections and uses auxiliary information from a FER network. The experiments are performed on the EmotionNet and DISFA datasets. The experiments show the usefulness of facial expression information for AU detection. The proposed network achieves state-of-the-art results on the two datasets. Analysis of the results for cross dataset protocol shows the effectiveness of the network. | en_US |
dc.language.iso | en_US | en_US |
dc.title | Expression empowered residen network for facial action unit detection | en_US |
dc.type | Article | en_US |