Automatic Facial Skin Feature Detection for Everyone
This work addresses the need for accessible skin condition assessment for health and cosmetic applications, but it is incremental as it applies an existing method (Unet++) to new data with improved annotation.
The paper tackled the problem of accurately detecting facial skin features like acne, pigmentation, and wrinkles in selfies across diverse skin tones and age groups, achieving robust detection under varying conditions through a two-phase annotation scheme.
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.