Facial expression recognition based on PCA and NMF
Lihong Zhao, Guibin Zhuang, Xinhe Xu · 2008
Principal Component Analysis (PCA) is a widely used technology about dimensional reduction. Non-negative Matrix Factorization (NMF), proposed by Lee and Sung, is a new image analysis method. In this paper, PCA and NMF are used to extract facial expression feature, and the recognition results of two methods are compared. We also try to process basic image matrix and weight matrix of PCA and make them as initialization of NMF. The experiments demonstrate that the method, based on the combination of PCA and NMF, has got a better recognition rate than PCA and NMF. The best recognition rate is 93.72%.