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Improved Cross-Dataset Facial Expression Recognition by Handling Data Imbalance and Feature Confusion

Sreenivas, M and Takamuku, S and Biswas, S and Chepuri, A and Vengatesan, B and Natori, N (2023) Improved Cross-Dataset Facial Expression Recognition by Handling Data Imbalance and Feature Confusion. In: 17th European Conference on Computer Vision, ECCV 2022, 23-27 October 2022, Tel Aviv, pp. 262-277.

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Official URL: https://doi.org/10.1007/978-3-031-25072-9_17


Facial Expression Recognition (FER) models trained on one dataset (source) usually do not perform well on a different dataset (target) due to the implicit domain shift between different datasets. In addition, FER data is naturally highly imbalanced, with a majority of the samples belonging to few expressions like neutral, happy and relatively fewer samples coming from expressions like disgust, fear, etc., which makes the FER task even more challenging. This class imbalance of the source and target data (which may be different), along with other factors like similarity of few expressions, etc., can result in unsatisfactory target classification performance due to confusion between the different classes. In this work, we propose an integrated module, termed DIFC, which can not only handle the source Data Imbalance, but also the Feature Confusion of the target data for improved classification of the target expressions.We integrate this DIFC module with an existing Unsupervised Domain Adaptation (UDA) approach to handle the domain shift and show that the proposed simple yet effective module can result in significant performance improvement on four benchmark datasets for Cross-Dataset FER (CD-FER) task. We also show that the proposed module works across different architectures and can be used with other UDA baselines to further boost their performance

Item Type: Conference Paper
Publication: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Publisher: Springer Science and Business Media Deutschland GmbH
Additional Information: The copyright for this article belongs to Springer Science and Business Media Deutschland GmbH.
Keywords: Classification (of information); Data handling; Face recognition, Class imbalance; Classification performance; Data feature; Data imbalance; Domain adaptation; Facial expression recognition; Performance; Recognition models; Target Classification; Unsupervised domain adaptation, Benchmarking
Department/Centre: Division of Electrical Sciences > Electrical Engineering
Date Deposited: 18 Apr 2023 10:21
Last Modified: 18 Apr 2023 10:21
URI: https://eprints.iisc.ac.in/id/eprint/81335

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