Cluster-level pseudo-labelling for source-free cross-domain facial expression recognition

Alessandro Conti (University of Trento),* Paolo Rota (University of Trento), Yiming Wang (Fondazione Bruno Kessler), Elisa Ricci (University of Trento)
The 33rd British Machine Vision Conference


Automatically understanding emotions from visual data is a fundamental task for human behaviour understanding. While models devised for Facial Expression Recognition (FER) have demonstrated excellent performances on many datasets, they often suffer from severe performance degradation when trained and tested on different datasets due to domain shift. In addition, as face images are considered highly sensitive data, the accessibility to large-scale datasets for model training is often denied. In this work, we tackle the above-mentioned problems by proposing the first Source-Free Unsupervised Domain Adaptation (SFUDA) method for FER. Our method exploits self-supervised pretraining to learn good feature representations from the target data and proposes a novel and robust cluster-level pseudo-labelling strategy that accounts for in-cluster statistics. We validate the effectiveness of our method in four adaptation setups, proving that it consistently outperforms existing SFUDA methods when applied to FER, and is on par with methods addressing FER in the UDA setting. We will release the code upon acceptance.



author    = {Alessandro Conti and Paolo Rota and Yiming Wang and Elisa Ricci},
title     = {Cluster-level pseudo-labelling for source-free cross-domain facial expression recognition},
booktitle = {33rd British Machine Vision Conference 2022, {BMVC} 2022, London, UK, November 21-24, 2022},
publisher = {{BMVA} Press},
year      = {2022},
url       = {}

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