论文标题
每个人的自动面部皮肤功能检测
Automatic Facial Skin Feature Detection for Everyone
论文作者
论文摘要
对面部皮肤状况的自动评估和理解有多种应用，包括早期发现潜在的健康问题，生活方式和饮食治疗，皮肤护理产品的建议等。野外的自拍照是一种极好的数据资源，可以使皮肤质量评估民主化，但要受到多种数据收集挑战的挑战。确保准确评估的关键是准确地检测出不同的肤色。我们提出了一种自动面部皮肤特征检测方法，该方法在野外自拍照的各种肤色和年龄组中都起作用。要具体而言，我们注释了痤疮，色素沉着和皱纹的位置，用于具有不同肤色颜色，严重程度和照明条件的自拍照图像。注释是在皮肤科医生的帮助下以两相计划进行的，以训练志愿者进行注释。我们使用UNET ++作为特征检测的网络体系结构。这项工作表明，两阶段注释方案可以牢固地检测痤疮，色素沉着和皱纹的准确位置，用于具有不同种族，肤色，肤色颜色，严重程度，年龄段和照明条件的自拍图像。
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.
