Human facial expression recognition based on learning subspace method
Xilin Chen, Sam Kwong, Yan Lu · 2002
The learning subspace method (LSM) is one of the most important methods for pattern recognition and it has been successfully used in many practical applications. 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.