Expression empowered ResiDen network for Facial Action Unit detection

Shreyank Jyoti, Garima Sharma, Abhinav Dhall

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearch

18 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationProceedings - 14th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2019
EditorsPeter Hancock, Richa Singh, Catherine Pelachaud, Vassilis Athitsos
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages8
ISBN (Electronic)9781728100890, 9781728100883
ISBN (Print)9781728100906
Publication statusPublished - 2019
Externally publishedYes
EventIEEE International Conference on Automatic Face and Gesture Recognition 2019 - Lille, France
Duration: 14 May 201918 May 2019
Conference number: 14th


ConferenceIEEE International Conference on Automatic Face and Gesture Recognition 2019
Abbreviated titleFG 2019
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