用手机自拍图远程评估皮肤水合与失水，让普通人也能自测皮肤状态。
AI-driven Remote Facial Skin Hydration and TEWL Assessment from Selfie Images: A Systematic Solution
- 基于自拍图像设计视觉变换模型，实现无接触皮肤评估。
- 提出对称对比正则化缓解数据标注不平衡问题，提升预测精度。
- 首个无需物理测量的自拍皮肤分析方案，适合日常护肤与健康管理。
皮肤健康与屏障功能密切相关，其保护作用体现在抵御环境侵害和水分流失上。皮肤水合（SH）与经表皮水分散失（TEWL）是量化评估屏障功能的关键生理指标，对公众日常皮肤监测、皮肤病诊断及个性化护肤具有重要价值。然而，传统测量需依赖专业设备，普通用户难以获取。为此，本文提出一套系统性解决方案，仅通过智能手机拍摄的自拍图像，实现远程估计SH与TEWL。该方案涵盖数据采集、预处理及创新的皮肤先验自适应视觉变换模型（Skin-Prior Adaptive Vision Transformer）用于回归分析。实验发现数据标注存在显著不平衡，因此提出基于对称性的对比正则化方法，有效降低模型偏差。本研究为首次探索不依赖物理测量的自拍图像皮肤评估，打通了计算机视觉与皮肤护理研究的桥梁，推动人工智能驱动的可及性皮肤分析在真实场景中的应用。
原文摘要 · Abstract (English)
Skin health and disease resistance are closely linked to the skin barrier function, which protects against environmental factors and water loss. Two key physiological indicators can quantitatively represent this barrier function: skin hydration (SH) and trans-epidermal water loss (TEWL). Measurement of SH and TEWL is valuable for the public to monitor skin conditions regularly, diagnose dermatological issues, and personalize their skincare regimens. However, these measurements are not easily accessible to general users unless they visit a dermatology clinic with specialized instruments. To tackle this problem, we propose a systematic solution to estimate SH and TEWL from selfie facial images remotely with smartphones. Our solution encompasses multiple stages, including SH/TEWL data collection, data preprocessing, and formulating a novel Skin-Prior Adaptive Vision Transformer model for SH/TEWL regression. Through experiments, we identified the annotation imbalance of the SH/TEWL data and proposed a symmetric-based contrastive regularization to reduce the model bias due to the imbalance effectively. This work is the first study to explore skin assessment from selfie facial images without physical measurements. It bridges the gap between computer vision and skin care research, enabling AI-driven accessible skin analysis for broader real-world applications.
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