Studying the effectiveness of using linear subspace techniques to improve SVM classifiers in facial image classification
In this paper we investigate the potential benefits of combining, within a classification task, a discriminant linear subspace feature extraction technique, namely Discriminant Nonnegative Matrix Factorization (Discriminant NMF or DNMF), with a Support Vector Machine (SVM) classifier. The aim was to investigate whether this combination provides better classification results compared to a template matching method operating on the DNMF space or on the raw data and an SVM classifier operating on the raw data, when applied on the frontal facial pose recognition problem. The latter is a two-class problem (frontal and non-frontal facial images). DNMF is based on a supervised training procedure and works by imposing additional criteria on the NMF objective function that aim at increasing class seperability in the lower dimensionality space. Results on face images extracted from the XM2VTS dataset show that feeding the DNMF subspace data into the SVM is the approach that provides the best results.