TY - GEN
T1 - Facial landmark detection in uncontrolled conditions
AU - Efraty, B.
AU - Huang, C.
AU - Shah, S. K.
AU - Kakadiaris, I. A.
PY - 2011
Y1 - 2011
N2 - Facial landmark detection is a fundamental step for many tasks in computer vision such as expression recognition and face alignment. In this paper, we focus on the detection of landmarks under realistic scenarios that include pose, illumination and expression challenges as well as blur and low-resolution input. In our approach, an n-point shape of point-landmarks is represented as a union of simpler polygonal sub-shapes. The core idea of our method is to find the sequence of deformation parameters simultaneously for all sub-shapes that transform each point-landmark into its target landmark location. To accomplish this task, we introduce an agglomerate of fern regressors. To optimize the convergence speed and accuracy we take advantage of search localization using component-landmark detectors, multi-scale analysis and learning of point cloud dynamics. Results from extensive experiments on facial images from several challenging publicly available databases demonstrate that our method (ACFeR) can reliably detect landmarks with accuracy comparable to commercial software and other state-of-the-art methods.
AB - Facial landmark detection is a fundamental step for many tasks in computer vision such as expression recognition and face alignment. In this paper, we focus on the detection of landmarks under realistic scenarios that include pose, illumination and expression challenges as well as blur and low-resolution input. In our approach, an n-point shape of point-landmarks is represented as a union of simpler polygonal sub-shapes. The core idea of our method is to find the sequence of deformation parameters simultaneously for all sub-shapes that transform each point-landmark into its target landmark location. To accomplish this task, we introduce an agglomerate of fern regressors. To optimize the convergence speed and accuracy we take advantage of search localization using component-landmark detectors, multi-scale analysis and learning of point cloud dynamics. Results from extensive experiments on facial images from several challenging publicly available databases demonstrate that our method (ACFeR) can reliably detect landmarks with accuracy comparable to commercial software and other state-of-the-art methods.
UR - http://www.scopus.com/inward/record.url?scp=84862955725&partnerID=8YFLogxK
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U2 - 10.1109/IJCB.2011.6117477
DO - 10.1109/IJCB.2011.6117477
M3 - Conference contribution
AN - SCOPUS:84862955725
SN - 9781457713583
T3 - 2011 International Joint Conference on Biometrics, IJCB 2011
BT - 2011 International Joint Conference on Biometrics, IJCB 2011
T2 - 2011 International Joint Conference on Biometrics, IJCB 2011
Y2 - 11 October 2011 through 13 October 2011
ER -