Privacy-Preserving Clothing Classification using Vision Transformer for Thermal Comfort Estimation
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arXiv
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866917445124489216 |
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| author | Chuman, Tatsuya Udagawa, Yousuke Kiya, Hitoshi |
| author_facet | Chuman, Tatsuya Udagawa, Yousuke Kiya, Hitoshi |
| contents | A privacy-preserving clothing classification scheme is presented to enable secure occupant-centric control (OCC) systems. Although the utilization of camera images for HVAC control has been widely studied to optimize thermal comfort, privacy protection of occupant images has not been considered in prior works. While various privacy-preserving methods have been proposed for image classification, applying conventional schemes results in severe accuracy degradation. In this paper, we introduce a privacy-preserving classification method using Vision Transformer (ViT) applied to clothing insulation estimation. In an experiment using the DeepFashion dataset categorized by clothing insulation, while the conventional pixel-based method suffers a severe accuracy drop, our scheme maintains a high accuracy on encrypted images, showing no degradation from plain images across all categories. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2604_26184 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Privacy-Preserving Clothing Classification using Vision Transformer for Thermal Comfort Estimation Chuman, Tatsuya Udagawa, Yousuke Kiya, Hitoshi Computer Vision and Pattern Recognition Cryptography and Security A privacy-preserving clothing classification scheme is presented to enable secure occupant-centric control (OCC) systems. Although the utilization of camera images for HVAC control has been widely studied to optimize thermal comfort, privacy protection of occupant images has not been considered in prior works. While various privacy-preserving methods have been proposed for image classification, applying conventional schemes results in severe accuracy degradation. In this paper, we introduce a privacy-preserving classification method using Vision Transformer (ViT) applied to clothing insulation estimation. In an experiment using the DeepFashion dataset categorized by clothing insulation, while the conventional pixel-based method suffers a severe accuracy drop, our scheme maintains a high accuracy on encrypted images, showing no degradation from plain images across all categories. |
| title | Privacy-Preserving Clothing Classification using Vision Transformer for Thermal Comfort Estimation |
| topic | Computer Vision and Pattern Recognition Cryptography and Security |
| url | https://arxiv.org/abs/2604.26184 |