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2017 | Supplement | Chapter

Improving Facial Landmark Detection via a Super-Resolution Inception Network

Authors : Martin Knoche, Daniel Merget, Gerhard Rigoll

Published in: Pattern Recognition

Publisher: Springer International Publishing

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Abstract

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 \(50\,\times \,50\,\mathrm {px}\). 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.

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Title: Improving Facial Landmark Detection via a Super-Resolution Inception Network
Authors: Martin Knoche
Daniel Merget
Gerhard Rigoll
Publisher: Springer International Publishing
Book: Pattern Recognition
Print ISBN: 978-3-319-66708-9

Electronic ISBN: 978-3-319-66709-6

Copyright Year: 2017
DOI: https://doi.org/10.1007/978-3-319-66709-6_20

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