Emotion Recognition from Non-Frontal Facial Images

Wenming Zheng, Hao Tang, Thomas S. Huang

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

This chapter reviews the recent advances of the current non-frontal facial emotion recognition methods, including the three-dimensional (3D) facial expression recognition and multiview facial expression recognition. It first gives a brief introduction of the facial emotion recognition methods. The chapter then briefly reviews the facial expression databases that are commonly used for the non-frontal facial emotion recognition researches. The method of using of 3D face model for face recognition has been proven to achieve better recognition results than 2D facial images due to its robustness to the poses, scales, and lighting variations. To perform the emotion classification, one may choose a classifier, for example, the support vector machine or Adaboost, and then classify each 3D face model into one of the basic emotion categories based on the geometric features.

Original languageEnglish (US)
Title of host publicationEmotion Recognition
Subtitle of host publicationA Pattern Analysis Approach
PublisherWiley
Pages183-213
Number of pages31
ISBN (Electronic)9781118910566
ISBN (Print)9781118130667
DOIs
StatePublished - Jan 2 2015

Keywords

  • Facial expression databases
  • Multiview facial expression recognition
  • Non-frontal facial emotion recognition methods
  • Three-dimensional (3D) facial expression recognition

ASJC Scopus subject areas

  • Engineering(all)
  • Computer Science(all)

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