3D facial expression recognition based on histograms of surface differential quantities

Huibin Li, Jean-Marie Morvan, Liming Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

32 Scopus citations

Abstract

3D face models accurately capture facial surfaces, making it possible for precise description of facial activities. In this paper, we present a novel mesh-based method for 3D facial expression recognition using two local shape descriptors. To characterize shape information of the local neighborhood of facial landmarks, we calculate the weighted statistical distributions of surface differential quantities, including histogram of mesh gradient (HoG) and histogram of shape index (HoS). Normal cycle theory based curvature estimation method is employed on 3D face models along with the common cubic fitting curvature estimation method for the purpose of comparison. Based on the basic fact that different expressions involve different local shape deformations, the SVM classifier with both linear and RBF kernels outperforms the state of the art results on the subset of the BU-3DFE database with the same experimental setting. © 2011 Springer-Verlag.
Original languageEnglish (US)
Title of host publicationAdvanced Concepts for Intelligent Vision Systems
PublisherSpringer Nature
Pages483-494
Number of pages12
ISBN (Print)9783642236860
DOIs
StatePublished - 2011

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint Dive into the research topics of '3D facial expression recognition based on histograms of surface differential quantities'. Together they form a unique fingerprint.

Cite this