Cascaded face alignment via intimacy definition feature

Hailiang Li, Kin Man Lam, Man Yau Chiu, Kangheng Wu, Zhibin Lei

Research output: Journal article publicationJournal articleAcademic researchpeer-review

4 Citations (Scopus)

Abstract

Recent years have witnessed the emerging popularity of regression-based face aligners, which directly learn mappings between facial appearance and shape-increment manifolds. We propose a randomforest based, cascaded regression model for face alignment by using a locally lightweight feature, namely intimacy definition feature. This feature is more discriminative than the pose-indexed feature, more efficient than the histogram of oriented gradients feature and the scale-invariant feature transform feature, and more compact than the local binary feature (LBF). Experimental validation of our algorithm shows that our approach achieves state-of-the-art performance when testing on some challenging datasets. Compared with the LBFbased algorithm, our method achieves about twice the speed, 20% improvement in terms of alignment accuracy and saves an order of magnitude on memory requirement.
Original languageEnglish
Article number053024
JournalJournal of Electronic Imaging
Volume26
Issue number5
DOIs
Publication statusPublished - 1 Sep 2017

Keywords

  • cascaded face alignment
  • intimacy definition feature
  • random forest

ASJC Scopus subject areas

  • Atomic and Molecular Physics, and Optics
  • Computer Science Applications
  • Electrical and Electronic Engineering

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