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This research demonstrates how self-supervised learning on Vision Transformers can drastically reduce labeling costs for facial expression recognition using optomyographic smart glasses, enabling more practical privacy-preserving health monitoring. By achieving superior performance with only 5% of labeled data compared to models trained on full datasets, the study offers a scalable pathway for deploying personalized affect sensing in real-world scenarios.
Self-Supervised Pretraining for Label-Efficient Facial Expression Recognition using Smart Glasses
arXiv:2611.05711v1 Announce Type: new Abstract: Facial Expression Recognition (FER) on optomyographic (OMG) smart glasses such as OCOsense enables privacy-preserving, camera-free affect sensing for health and well-being. Most FER systems are fully supervised, i.e., they rely either on hand-crafted expert features with classical machine learning, or on end-to-end deep models trained from labelled data. Both depend on large quantities of labelled expressions, which are costly to collect and limit deployment in real-world, personalized settings. We investigate Self-Supervised Learning (SSL) as a route to label-efficient FER on these glasses. We pretrain a Vision Transformer with a masked-autoencoder objective on unlabelled OCOsense recordings and finetune it for five-class FER under person-independent cross-validation. Across label budgets from 5% to 100%, pretraining consistently improves performance; with only 5% of the labels, the pretrained model outperforms the from-scratch model trained with all labels. Our results show that SSL pretraining is a practical way to reduce labelling cost for smart eyewear FER, and we discuss the implications for future personalization and continual adaptation.
Abstract:Facial Expression Recognition (FER) on optomyographic (OMG) smart glasses such as OCOsense enables privacy-preserving, camera-free affect sensing for health and well-being. Most FER systems are fully supervised, i.e., they rely either on hand-crafted expert features with classical machine learning, or on end-to-end deep models trained from labelled data. Both depend on large quantities of labelled expressions, which are costly to collect and limit deployment in real-world, personalized settings. We investigate Self-Supervised Learning (SSL) as a route to label-efficient FER on these glasses. We pretrain a Vision Transformer with a masked-autoencoder objective on unlabelled OCOsense recordings and finetune it for five-class FER under person-independent cross-validation. Across label budgets from 5% to 100%, pretraining consistently improves performance; with only 5% of the labels, the pretrained model outperforms the from-scratch model trained with all labels. Our results show that SSL pretraining is a practical way to reduce labelling cost for smart eyewear FER, and we discuss the implications for future personalization and continual adaptation.
7 pages, 5 figures, 2 tables. Best Paper Award at the SEPH 2026 workshop, UbiComp Companion '26
Artificial Intelligence (cs.AI)
arXiv:2611.05711 [cs.AI]
(or arXiv:2611.05711v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2611.05711 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
https://doi.org/10.1145/3798063.3839146 Focus to learn more DOI(s) linking to related resources
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