  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.
