Learning to validate the quality of detected landmarks

Wolfgang Fuhl; Enkelejda Kasneci

doi:10.1117/12.2559517

31 January 2020 Learning to validate the quality of detected landmarks

Wolfgang Fuhl, Enkelejda Kasneci

Proceedings Volume 11433, Twelfth International Conference on Machine Vision (ICMV 2019); 114330E (2020) https://doi.org/10.1117/12.2559517
Event: Twelfth International Conference on Machine Vision, 2019, Amsterdam, Netherlands

Abstract

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.

Citation Download Citation

Wolfgang Fuhl and Enkelejda Kasneci "Learning to validate the quality of detected landmarks", Proc. SPIE 11433, Twelfth International Conference on Machine Vision (ICMV 2019), 114330E (31 January 2020); https://doi.org/10.1117/12.2559517

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