Accession Number:

AD1158305

Title:

Constrained Local Neural Fields for Robust Facial Landmark Detection in the Wild

Descriptive Note:

[Technical Report, Research Paper]

Corporate Author:

UNIVERSITY OF SOUTHERN CALIFORNIA LOS ANGELES

Report Date:

2013-12-02

Pagination or Media Count:

8

Abstract:

Facial feature detection algorithms have seen great progress over the recent years. However, they still struggle in poor lighting conditions and in the presence of extreme pose or occlusions. We present the Constrained Local Neural Field model for facial landmark detection. Our model includes two main novelties. First, we introduce a probabilistic patch expert landmark detector that can learn non-linear and spatial relationships between the input pixels and the probability of a landmark being aligned. Secondly, our model is optimised using a novel Non-uniform Regularised Landmark Mean-Shift optimisation technique, which takes into account the reliabilities of each patch expert. We demonstrate the benefit of our approach on a number of publicly available datasets over other state-of-the-art approaches when performing landmark detection in unseen lighting conditions and in the wild.

Descriptors:

Subject Categories:

  • Psychology

Distribution Statement:

[A, Approved For Public Release]