# Automatic Facial Skin Feature Detection for Everyone

arXiv: https://arxiv.org/abs/2203.16056 · HTML: https://mlsanity.com/paper/2203.16056/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 39 references that are in the map.
- First posted on arXiv 30 Mar 2022.

## Builds on (most cited first, 4 of 4)

- [UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation](https://mlsanity.com/paper/1912.05074/) (2019)
- [Learning Deconvolution Network for Semantic Segmentation](https://mlsanity.com/paper/1505.04366/) (2015)
- [ACNet: Mask-Aware Attention with Dynamic Context Enhancement for Robust Acne Detection](https://mlsanity.com/paper/2105.14891/) (2021)
- [An Automatic Diagnosis Method of Facial Acne Vulgaris Based on Convolutional Neural Network](https://mlsanity.com/paper/1711.04481/) (2017)

## Built on by (most cited first, 4 of 4)

- [Skin Deep: Investigating Subjectivity in Skin Tone Annotations for Computer Vision Benchmark Datasets](https://mlsanity.com/paper/2305.09072/) (2023)
- [Controllable and Gradual Facial Blemishes Retouching via Physics-Based Modelling](https://mlsanity.com/paper/2406.13227/) (2024)
- [AI-driven Remote Facial Skin Hydration and TEWL Assessment from Selfie Images: A Systematic Solution](https://mlsanity.com/paper/2509.06282/) (2025)
- [SkinAgent AI: A Safety-Grounded Multimodal Agentic Framework for Non-Diagnostic Skincare Support](https://mlsanity.com/paper/2609.29341/) (2026)

## Abstract (arXiv)

Automatic assessment and understanding of facial skin condition have several applications, including the early detection of underlying health problems, lifestyle and dietary treatment, skin-care product recommendation, etc. Selfies in the wild serve as an excellent data resource to democratize skin quality assessment, but suffer from several data collection challenges.The key to guaranteeing an accurate assessment is accurate detection of different skin features. We present an automatic facial skin feature detection method that works across a variety of skin tones and age groups for selfies in the wild. To be specific, we annotate the locations of acne, pigmentation, and wrinkle for selfie images with different skin tone colors, severity levels, and lighting conditions. The annotation is conducted in a two-phase scheme with the help of a dermatologist to train volunteers for annotation. We employ Unet++ as the network architecture for feature detection. This work shows that the two-phase annotation scheme can robustly detect the accurate locations of acne, pigmentation, and wrinkle for selfie images with different ethnicities, skin tone colors, severity levels, age groups, and lighting conditions.