We propose an improvement to the landmark validity loss. Landmark detection is widely used in head pose estimation, eyelid shape extraction, as well as pupil and iris segmentation. There are numerous additional applications where landmark detection is used to estimate the shape of complex objects. One part of this process is the accurate and fine-grained detection of the shape. The other part is the validity or inaccuracy per landmark, which can be used to detect unreliable areas, where the shape possibly does not fit, and to improve the accuracy of the entire shape extraction by excluding inaccurate landmarks. We propose a normalization in the loss formulation, which improves the accuracy of the entire approach due to the numerical balance of the normalized inaccuracy. In addition, we propose a margin for the inaccuracy to reduce the impact of gradients, which are produced by negligible errors close to the ground truth.

翻译：我们提出了一种对地标有效性损失的改进。地标检测广泛用于头部姿态估计、眼睑形状提取以及瞳孔和虹膜分割。还有许多其他应用场景中，地标检测被用于估计复杂物体的形状。该过程的一部分是对形状进行精确且细粒度的检测，另一部分是每个地标点的有效性或不准确性，这可用于检测可能存在形状不匹配的不可靠区域，并通过排除不准确的地标点来提高整体形状提取的精度。我们提出了一种损失函数中的归一化方法，由于归一化后的不准确性数值平衡，该方法提升了整体方法的精度。此外，我们为不准确性引入了一个边界，以减小接近真实值的微小误差所产生梯度的影响。