Recognition of facial expressions using HMM with continuous output probabilities

Takahiro Otsuka, Jun Ohya

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

26 Citations (Scopus)

Abstract

Facial expression recognition is an important technology fundamental to realize intelligent image coding systems and advanced man-machine interfaces in visual communication systems. In computer vision field, many techniques have been developed to recognize facial expressions. However, most of those techniques are based on static features extracted from one or two still images. Those techniques are not robust against noise and cannot recognize subtle changes in facial expressions. In this paper we use hidden Markov models (HMM) with continuous output probabilities to extract a temporal pattern of facial motion. In order to improve the recognition performance, we propose a new feature obtained from wavelet transform coefficients. For the evaluation, we use 180 image sequences taken from three male subjects. Using these image sequences, the recognition rate for user trained mode achieves 98% compared with 84% using our previous method. The recognition rate for user independent mode achieves 84% when the expressions are restricted to four expressions.

Original languageEnglish
Title of host publicationRobot and Human Communication - Proceedings of the IEEE International Workshop
Editors Anon
Place of PublicationPiscataway, NJ, United States
PublisherIEEE
Pages323-328
Number of pages6
Publication statusPublished - 1996
Externally publishedYes
EventProceedings of the 1996 5th IEEE International Workshop on Robot and Human Communication, RO-MAN - Tsukuba, Jpn
Duration: 1996 Nov 111996 Nov 14

Other

OtherProceedings of the 1996 5th IEEE International Workshop on Robot and Human Communication, RO-MAN
CityTsukuba, Jpn
Period96/11/1196/11/14

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ASJC Scopus subject areas

  • Hardware and Architecture
  • Software

Cite this

Otsuka, T., & Ohya, J. (1996). Recognition of facial expressions using HMM with continuous output probabilities. In Anon (Ed.), Robot and Human Communication - Proceedings of the IEEE International Workshop (pp. 323-328). IEEE.