Audiovisual Emotion Recognition Using Semi-Coupled Hidden Markov Model with State-Based Alignment Strategy

Chung Hsien Wu, Jen Chun Lin, Wen Li Wei

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

This chapter introduces the current data fusion strategies among audiovisual signals for bimodal emotion recognition. Face detection, in the chapter, is performed based on the adaboost cascade face detector and can be used to provide initial facial position and reduce the time for error convergence in feature extraction. In the chapter, active appearance model (AAM) is employed to extract the 68 labeled facial feature points (FPs) from 5 facial regions including eyebrow, eye, nose, mouth, and facial contours for later facial animation parameters (FAPs) calculation. Three kinds of primary prosodic features are adopted, including pitch, energy, and formants F1-F5 in each speech frame for emotion recognition. Finally, a semi-coupled hidden Markov model (SC-HMM) is proposed for emotion recognition based on state-based alignment strategy for audiovisual bimodal features.

Original languageEnglish
Title of host publicationEmotion Recognition
Subtitle of host publicationA Pattern Analysis Approach
Publisherwiley
Pages493-513
Number of pages21
ISBN (Electronic)9781118910566
ISBN (Print)9781118130667
DOIs
Publication statusPublished - 2015 Jan 2

All Science Journal Classification (ASJC) codes

  • Engineering(all)
  • Computer Science(all)

Fingerprint Dive into the research topics of 'Audiovisual Emotion Recognition Using Semi-Coupled Hidden Markov Model with State-Based Alignment Strategy'. Together they form a unique fingerprint.

Cite this