Normalized Validity Scores for DNNs in Regression based Eye Feature Extraction
This work addresses incremental improvements in landmark detection for applications like head pose estimation and eye analysis.
The authors tackled the problem of improving landmark detection accuracy in eye feature extraction by proposing a normalized validity loss and a margin for inaccuracy, resulting in enhanced overall shape extraction accuracy.
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.