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3D facial landmark detection: How to deal with head rotations?

 
: Schwarz, A.; Wacker, E.-S.; Martin, Manuel; Sarfraz, M.S.; Stiefelhagen, R.

:

Gall, Jürgen (Ed.):
Pattern recognition. 37th German conference, GCPR 2015 : Aachen, Germany, October 7-10, 2015; Proceedings
Cham: Springer International Publishing, 2015 (Lecture Notes in Computer Science 9358)
ISBN: 978-3-319-24946-9 (Print)
ISBN: 978-3-319-24947-6 (Online)
S.424-434
German Conference on Pattern Recognition (GCPR) <37, 2015, Aachen>
Englisch
Konferenzbeitrag
Fraunhofer IOSB ()
face recognition; NAT

Abstract
3D facial landmark detection is important for applications like facial expression analysis and head pose estimation. However, accurate estimation of facial landmarks in 3D with head rotations is still challenging due to perspective variations. Current state-of-the-art methods are based on random forests. These methods rely on a large amount of training data covering the whole range of head rotations. We present a method based on regression forests which can handle rotations even if they are not included in the training data. To achieve this, we modify both the weak predictors of the tree and the leaf node regressors to adapt to head rotations better. Our evaluation on two benchmark datasets, Bosphorus and FRGC v2, shows that our method outperforms state-of-the-art methods with respect to head rotations, if trained solely on frontal faces

: http://publica.fraunhofer.de/dokumente/N-374917.html