Application of non-negative and local non negative matrix factorization to facial expression recognition

Two image representation approaches called non-negative matrix factorization (NMF) and local non-negative matrix factorization (LNMF) have been applied to two facial databases for recognizing six basic facial expressions. A principal component analysis (PCA) approach was performed as well for facial expression recognition for comparison purposes. We found that, for the first database, LNMF outperforms both PCA and NMF, while NMF produces the poorest recognition performance. Results are approximately the same for the second database, with slightly performance improvement on behalf of NMF.