Abstract：The traditional locally linear embedding (LLE) algorithm doesn’t take into consideration the class label information of training samples while the supervised LLE (SLLE) algorithm treats the different between classes equally which is inappropriate for facial expression recognition. Taking the differences between expressions into account, a new supervised locally linear embedding (SLLE) algorithm called expression related SLLE (ERSLLE) is designed which uses different weights for sample distance calculation in determining neighborhood samples. The proposed algorithm is applied to the facial expression recognition on the Japanese female facial expression database (JAFFE). The results show that the proposed algorithm is effective and superior to the traditional LLE and SLLE. Better performance is obtained for facial expression recognition in a certain range of the number k of the nearest neighborhood, compared with LLE and SLLE.
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2010, Vol. 23 
