TY - GEN
T1 - Improving facial landmark detection via a super-resolution inception network
AU - Knoche, Martin
AU - Merget, Daniel
AU - Rigoll, Gerhard
N1 - Publisher Copyright:
© Springer International Publishing AG 2017.
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85029576272&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-66709-6_20
DO - 10.1007/978-3-319-66709-6_20
M3 - Conference contribution
AN - SCOPUS:85029576272
SN - 9783319667089
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 239
EP - 251
BT - Pattern Recognition - 39th German Conference, GCPR 2017, Proceedings
A2 - Roth, Volker
A2 - Vetter, Thomas
PB - Springer Verlag
T2 - 39th German Conference on Pattern Recognition, GCPR 2017
Y2 - 12 September 2017 through 15 September 2017
ER -