Robust head pose estimation via supervised manifold learning

Chao Wang, Xubo Song

Research output: Contribution to journalArticle

16 Scopus citations

Abstract

Head poses can be automatically estimated using manifold learning algorithms, with the assumption that with the pose being the only variable, the face images should lie in a smooth and low-dimensional manifold. However, this estimation approach is challenging due to other appearance variations related to identity, head location in image, background clutter, facial expression, and illumination. To address the problem, we propose to incorporate supervised information (pose angles of training samples) into the process of manifold learning. The process has three stages: neighborhood construction, graph weight computation and projection learning. For the first two stages, we redefine inter-point distance for neighborhood construction as well as graph weight by constraining them with the pose angle information. For Stage 3, we present a supervised neighborhood-based linear feature transformation algorithm to keep the data points with similar pose angles close together but the data points with dissimilar pose angles far apart. The experimental results show that our method has higher estimation accuracy than the other state-of-art algorithms and is robust to identity and illumination variations.

Original languageEnglish (US)
Pages (from-to)15-25
Number of pages11
JournalNeural Networks
Volume53
DOIs
StatePublished - May 1 2014

Keywords

  • Graph weight
  • Inter-point distance
  • Projection learning
  • Robust head pose estimation
  • Supervised manifold learning

ASJC Scopus subject areas

  • Cognitive Neuroscience
  • Artificial Intelligence

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