  We present a new loss function for the validation of image landmarks detected
via Convolutional Neural Networks (CNN). The network learns to estimate how
accurate its landmark estimation is. This loss function is applicable to all
regression-based location estimations and allows the exclusion of unreliable
landmarks from further processing. In addition, we formulate a novel batch
balancing approach which weights the importance of samples based on their
produced loss. This is done by computing a probability distribution mapping on
an interval from which samples can be selected using a uniform random selection
scheme. We conducted experiments on the 300W, AFLW, and WFLW facial landmark
datasets. In the first experiments, the influence of our batch balancing
approach is evaluated by comparing it against uniform sampling. In addition, we
evaluated the impact of the validation loss on the landmark accuracy based on
uniform sampling. The last experiments evaluate the correlation of the
validation signal with the landmark accuracy. All experiments were performed
for all three datasets.
