Wearing a mask is a strong protection against the COVID-19 pandemic, even though the vaccine has been successfully developed and is widely available. However, many people wear them incorrectly. This observation prompts us to devise an automated approach to monitor the condition of people wearing masks. Unlike previous studies, our work goes beyond mask detection; it focuses on generating a personalized demonstration on proper mask-wearing, which helps people use masks better through visual demonstration rather than text explanation. The pipeline starts from the detection of face covering. For images where faces are improperly covered, our mask overlay module incorporates statistical shape analysis (SSA) and dense landmark alignment to approximate the geometry of a face and generates corresponding face-covering examples. Our results show that the proposed system successfully identifies images with faces covered properly. Our ablation study on mask overlay suggests that the SSA model helps to address variations in face shapes, orientations, and scales. The final face-covering examples, especially half profile face images, surpass previous arts by a noticeable margin.
翻译:戴面罩是抵御COVID-19大流行的有力保护手段,尽管疫苗已经成功开发并广泛提供,但许多人戴得不正确。 观察结果促使我们设计一种自动方法来监测戴面罩的人的状况。 与以往的研究不同, 我们的工作超越了蒙面检测; 重点是就戴面罩进行个性化演示, 这有助于人们通过视觉演示而不是文字解释更好地使用遮面罩。 输油管从检测面罩开始。 对于面部覆盖不当的图像, 我们的蒙面覆盖模块包含统计形状分析(SSA)和密集的标志性调整, 以近似面部几何特征, 并生成相应的面罩示例。 我们的结果显示, 提议的系统成功地辨别了面罩面部覆盖的图像。 我们对蒙面罩覆盖面罩的反动研究表明, SSA模型有助于解决面形状、 方向 和 规模 的变异。 最后的面罩面罩面模型, 特别是半面孔图像, 以显著的边缘超过以前的艺术。