Deformable face ensemble alignment with robust grouped-L1 anchors


Autoria(s): Cheng, Xin; Fookes, Clinton B.; Sridharan, Sridha; Saragih, Jason; Lucey, Simon
Data(s)

22/04/2013

Resumo

Many methods exist at the moment for deformable face fitting. A drawback to nearly all these approaches is that they are (i) noisy in terms of landmark positions, and (ii) the noise is biased across frames (i.e. the misalignment is toward common directions across all frames). In this paper we propose a grouped $\mathcal{L}1$-norm anchored method for simultaneously aligning an ensemble of deformable face images stemming from the same subject, given noisy heterogeneous landmark estimates. Impressive alignment performance improvement and refinement is obtained using very weak initialization as "anchors".

Identificador

http://eprints.qut.edu.au/57113/

Publicador

IEEE

Relação

DOI:http://dx.doi.org/10.1109/FG.2013.6553739

Cheng, Xin, Fookes, Clinton B., Sridharan, Sridha, Saragih, Jason, & Lucey, Simon (2013) Deformable face ensemble alignment with robust grouped-L1 anchors. In 10th IEEE Conference on Automatic Face and Gesture Recognition (FG2013), IEEE, Shanghai, China, pp. 1-7.

http://purl.org/au-research/grants/ARC/DP1110827

Fonte

School of Electrical Engineering & Computer Science; Information Security Institute; Science & Engineering Faculty

Palavras-Chave #080100 ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING #080104 Computer Vision #090609 Signal Processing
Tipo

Conference Paper