Accurate facial landmark detection under occlusion remains challenging, especially for human-like faces with large appearance variation and rotation-driven self-occlusion. Existing detectors typically localize landmarks while handling occlusion implicitly, without predicting per-point visibility that downstream applications can benefits. We present OccFace, an occlusion-aware framework for universal human-like faces, including humans, stylized characters, and other non-human designs. OccFace adopts a unified dense 100-point layout and a heatmap-based backbone, and adds an occlusion module that jointly predicts landmark coordinates and per-point visibility by combining local evidence with cross-landmark context. Visibility supervision mixes manual labels with landmark-aware masking that derives pseudo visibility from mask-heatmap overlap. We also create an occlusion-aware evaluation suite reporting NME on visible vs. occluded landmarks and benchmarking visibility with Occ AP, F1@0.5, and ROC-AUC, together with a dataset annotated with 100-point landmarks and per-point visibility. Experiments show improved robustness under external occlusion and large head rotations, especially on occluded regions, while preserving accuracy on visible landmarks.

翻译：遮挡条件下的精确人脸关键点检测仍具挑战性，尤其对于类人面孔，其外观变化大且旋转易导致自遮挡。现有检测器通常在隐式处理遮挡的同时定位关键点，未能预测下游应用可受益的逐点可见性。我们提出OccFace，一个面向通用类人面孔（包括人类、风格化角色及其他非人类设计）的遮挡感知框架。OccFace采用统一的密集100点布局与基于热图的骨干网络，并添加一个遮挡模块，通过结合局部证据与跨关键点上下文，联合预测关键点坐标与逐点可见性。可见性监督混合了人工标注与基于关键点的掩码生成，后者通过掩码-热图重叠推导伪可见性。我们还创建了一个遮挡感知评估套件，报告可见与遮挡关键点的归一化平均误差，并使用Occ AP、F1@0.5和ROC-AUC指标对可见性进行基准测试，同时提供了一个标注有100点关键点及逐点可见性的数据集。实验表明，该方法在外部遮挡与大角度头部旋转下（尤其在遮挡区域）鲁棒性显著提升，同时保持了对可见关键点的检测精度。