Automatic Facial Skin Feature Detection for Everyone

TL;DR

This paper introduces an automatic facial skin feature detection method using Unet++ that accurately identifies acne, pigmentation, and wrinkles across diverse skin tones, ages, and lighting conditions in selfies from the wild.

Contribution

It presents a novel two-phase annotation scheme with dermatologist involvement and demonstrates the effectiveness of Unet++ for robust skin feature detection in diverse selfie images.

Findings

Accurately detects skin features across various ethnicities and skin tones.

Robust detection under different lighting conditions and severity levels.

Two-phase annotation improves annotation accuracy and model performance.

Abstract

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.