Modern convolutional neural networks for facial landmark detection have become increasingly robust against occlusions, lighting conditions and pose variations. With the predictions being close to pixel-accurate in some cases, intuitively, the input resolution should be as high as possible. We verify this intuition by thoroughly analyzing the impact of low image resolution on landmark prediction performance. Indeed, performance degradations are already measurable for faces smaller than 50×50px. In order to mitigate those degradations, a new super-resolution inception network architecture is developed which outperforms recent super-resolution methods on various data sets. By enhancing low resolution images with our model, we are able to improve upon the state of the art in facial landmark detection.
CITATION STYLE
Knoche, M., Merget, D., & Rigoll, G. (2017). Improving facial landmark detection via a super-resolution inception network. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10496 LNCS, pp. 239–251). Springer Verlag. https://doi.org/10.1007/978-3-319-66709-6_20
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