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A new sparse image representation algorithm applied to facial expression recognition

Domenii publicaţii > Ştiinţe informatice + Tipuri publicaţii > Articol în volumul unei conferinţe

Autori: I. Buciu and I. Pitas

Editorial: Proc. IEEE Workshop on Machine Learning for Signal Processing, Sao Luis, Brasil, p.539 – 548, 2004.


In this paper we present a novel algorithm for learning facial expressions in a supervised manner. This algorithm is derived from the local non-negative matrix factorization (LNMF) algorithm, which is an extension of non-negative matrix factorization (NMF) method. We call this newly proposed algorithm Discriminant Non-negative Matrix Factorization (DNMF). Given an image database, all these three algorithms decompose the database into basis images and their corresponding coefficients. This decomposition is computed differently for each method. The decomposition results are applied on facial images for the recognition of the six basic facial expressions. We found that our algorithm shows superior performance by achieving a higher recognition rate, when compared to NMF and LNMF. Moreover, we compared DNMF’s classification performance with independent component analysis (ICA) and Gabor approaches which are considered state of the approaches for facial expression analysis. We found they are outperformed by DNMF. The experiments were conducted for varying number of basis images (subspaces). The recognition rate of the proposedalgorithm seems to be more robust than that of other methods with respect to the choice of the number of basis images.

Cuvinte cheie: Non-negative matrix factorization, facial expresison recognition