Human facial expression recognition based on learning subspace method


Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

1 Citation (Scopus)


Learning subspace method (LSM) is one of the most important methods for pattern recognition and it has been successfully used in many practical applications. In this paper, we propose to use the LSM for human facial expression recognition. Seven expression subspaces are built for expression models. The idea of recognizing facial expression through a single static image is realized and the recognition rate as high as 89.5% is achieved. In order to make these expression subspaces more adaptive we can gradually learn them by using the averaged learning subspace method (ALSM). Experimental results also indicate that the recognition rate is over 90%. The dynamic characteristics of the projection vector sequence on these facial expression subspaces are also discussed in this paper.
Original languageEnglish
Title of host publicationIEEE International Conference on Multi-Media and Expo
Publication statusPublished - 2000
Externally publishedYes


Dive into the research topics of 'Human facial expression recognition based on learning subspace method'. Together they form a unique fingerprint.

Cite this