Ruan, D;
Yan, Y;
Chen, S;
Xue, J;
Wang, H;
(2020)
Deep Disturbance-disentangled Learning for Facial Expression Recognition.
In:
MM '20: Proceedings of the 28th ACM International Conference on Multimedia.
(pp. pp. 2833-2841).
ACM
Text
DelianRuan-ACMMM2020-accepted.pdf - Accepted Version Download (2MB) |
Abstract
To achieve effective facial expression recognition (FER), it is of great importance to address various disturbing factors, including pose, illumination, identity, and so on. However, a number of FER databases merely provide the labels of facial expression, identity, and pose, but lack the label information for other disturbing factors. As a result, many methods are only able to cope with one or two disturbing factors, ignoring the heavy entanglement between facial expression and multiple disturbing factors. In this paper, we propose a novel Deep Disturbance-disentangled Learning (DDL) method for FER. DDL is capable of simultaneously and explicitly disentangling multiple disturbing factors by taking advantage of multi-task learning and adversarial transfer learning. The training of DDL involves two stages. First, a Disturbance Feature Extraction Model (DFEM) is pre-trained to perform multi-task learning for classifying multiple disturbing factors on the large-scale face database (which has the label information for various disturbing factors). Second, a Disturbance-Disentangled Model (DDM), which contains a global shared sub-network and two task-specific (i.e., expression and disturbance) sub-networks, is learned to encode the disturbance-disentangled information for expression recognition. The expression sub-network adopts a multi-level attention mechanism to extract expression-specific features, while the disturbance sub-network leverages adversarial transfer learning to extract disturbance-specific features based on the pre-trained DFEM. Experimental results on both the in-the-lab FER databases (including CK+, MMI, and Oulu-CASIA) and the in-the-wild FER databases (including RAF-DB and SFEW) demonstrate the superiority of our proposed method compared with several state-of-the-art methods.
Type: | Proceedings paper |
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Title: | Deep Disturbance-disentangled Learning for Facial Expression Recognition |
Event: | ACM Multimedia 2020 |
Dates: | 12 October 2020 - 16 October 2020 |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.1145/3394171.3413907 |
Publisher version: | https://doi.org/10.1145/3394171.3413907 |
Language: | English |
Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science |
URI: | https://discovery.ucl.ac.uk/id/eprint/10110888 |
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