  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.
