Facial expression recognition using LNMF based on PPBTF
Yingjie Huang · Dalian Ligong Daxue xuebao · 2009
A new sort of face representation method — pixel-pattern-based texture feature(PPBTF)is adopted to extract features from facial images.A gray scale image is firstly transformed into a pattern map where edges and lines can be used for characterizing the facial texture information.Secondly,based on the pattern window,a feature vector is comprised of the numbers of the pixels belonging to each pattern.Then,the image basis functions obtained by local non-negative matrix factorization(LNMF)are used as the templates for pattern matching.Additionally,Adaboost and support vector machine(SVM)are adopted to classify facial expression.Experiments on the Cohn-Kanade database illustrate that the PPBTF combined LNMF is efficient for facial expression recognition,and experiments on the PIE and other database also verify the PPBTF's insensitivity to the lighting condition.Conclusively,the face representation method proposed is proved to be efficient and robust.