Domain adaptation for classifying spontaneous smile videos
Author Identifier
Syed Mohammed Shamsul Islam: https://orcid.org/0000-0002-3200-2903
Document Type
Conference Proceeding
Publication Title
Proceedings - 2024 25th International Conference on Digital Image Computing: Techniques and Applications, DICTA 2024
First Page
252
Last Page
259
Publisher
IEEE
School
School of Science
Funders
National Computational Infrastructure Australia and North South University Conference Travel and Research Grants (CTRG-21-SEPS-10)
Abstract
Distinguishing spontaneous and posed smiles has become an exciting topic due to its potential application in several sectors. However, it is a very challenging task, even for humans. Past researchers have proposed several semi and fully automatic approaches for smile classification. These approaches have explored both feature-based engineering and end-to-end deep neural network-based strategies. One major issue with past methods is the degradation of performance when deploying the model in a data domain different from the training domain, as smile patterns are different across diverse groups (e.g., young, adult, male, and female). In this paper, we present an end-to-end domain adaptation model to address these problems. We explore a new unsupervised domain adaptation application for smile veracity recognition. We propose an identity-invariant learning objective to align the training (source) data knowledge to the testing (target) data. Our approach penalizes identity information hidden in the feature space by enhancing sufficient distinctiveness among different smile phase features while maintaining inter-class cohesion. We have used UVA-NEMO, MMI, SPOS, and BBC datasets to validate the performance of our model and found that our domain adaptation approach outperforms the existing models by achieving state-of-the-art performance.
DOI
10.1109/DICTA63115.2024.00046
Access Rights
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Comments
Biswas, A., Hossain, M. Z., Yang, Y., Islam, S. M. S., Gedeon, T., & Rahman, S. (2024, November). Domain adaptation for classifying spontaneous smile videos. In 2024 International Conference on Digital Image Computing: Techniques and Applications (DICTA) (pp. 252-259). IEEE. https://doi.org/10.1109/DICTA63115.2024.00046