mlsanityData to 14 Oct 2026

AI-driven Remote Facial Skin Hydration and TEWL Assessment from Selfie Images: A Systematic Solution

arXiv 2509.06282 ↗First posted 8 Sep 2025cs.CVcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 39 of its 59 references that are in the map. First posted on arXiv 8 Sep 2025.

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39 of 59 references.

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