Privacy-Preserving Clothing Classification using Vision Transformer for Thermal Comfort Estimation
For HVAC control systems, this work addresses the privacy concern of occupant images while maintaining classification accuracy, enabling secure occupant-centric control.
The paper presents a privacy-preserving clothing classification method using Vision Transformer (ViT) for thermal comfort estimation in HVAC control. The scheme maintains high accuracy on encrypted images with no degradation from plain images across all categories, unlike conventional pixel-based methods that suffer severe accuracy drops.
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.