In this paper, an analysis of the effect of partial occlusion on facial expression recognition is investigated. The classification from partially occluded images in one of the six basic facial expressions is performed using a method based on Gabor-wavelets texture information extraction, a supervised image decomposition method based on Discriminant Non-negative Matrix Factorization and a shape based method that exploits the geometrical displacement

Plenty of methods have been proposed in order to discover latent variables (features) in data sets. Such approaches include the Principal Component Analysis (PCA), Independent Component Analysis (ICA), Factor Analysis (FA), etc., to mention only a few. A recently investigated approach to decompose a data set with a given dimensionality into a lower dimensional space is the so-called Non-negative Matrix Factorization (NMF). Its only requirement is

Vision based object recognition has attracted much interest in recent years due to its spread area of applications. Purely computer vision techniques, biologically motivated approaches or combined methods have been developed to tackle this task. Object recognition task based on three variants of non-negative matrix factorization techniques is investigated in this paper. The analysis is undertaken with respect to the recognition performances of the

Abstract—In this paper, two supervised methods for enhancing the classification accuracy of the Nonnegative Matrix Factorization (NMF) algorithm are presented. The idea is to extend the NMF algorithm in order to extract features that enforce not only the spatial locality, but also the separability between classes in a discriminant manner. The first method employs discriminant analysis in the features derived from NMF. In this way, a two-phase discriminant

Recently, three learning algorithms, namely non-negative matrix factorization (NMF), local non-negative matrix factorization (LNMF) and discriminant non-negative matrix factorization (DNMF) have been proposed to produce sparse image representations. However, when their input is a database of human facial images, they decompose the images into sparse representations with quite different degree of sparseness. Within a continuum of sparseness ranging

In this paper, we deal with the stability of support vector machines (SVMs) in classification tasks. We decompose the average prediction error of support vector machines into the bias and the variance terms, and we define the aggregation effect. By estimating the aforementioned terms with bootstrap smoothing techniques, we demonstrate that support vector machines are stable classifiers. To investigate the stability of the SVM several experiments

The paper presents a system that makes use of the fusion information paradigm to integrate two different sorts of information in order to improve the facial expression classification accuracy over a single feature based classification one. The Discriminant Non-negative Matrix Factorization (DNMF) approach is used to extract a first set of features and an automatically geometrical-based feature extraction algorithm is used for retrieving the second

Three techniques called non-negative matrix factorization (NMF), local non-negative matrix factorization (LNMF), and discriminant non-negative matrix factorization (DNMF), have been recently developed for decomposing a data matrix into non-negative factors named basis images and decomposition coefficients. Although these techniques are closely related to each other since they impose certain common non-negative constraints, the decomposition process

A subspace supervised learning algorithm named Discriminant Non-negative Matrix Factorization (DNMF) has been recently proposed for classifying human facial expressions. It decomposes images into a set of basis images and corresponding coefficients. Usually, the algorithm starts with random basis image and coefficient initialization. Then, at each iteration, both basis images and coefficients are updated to minimize the underlying cost function.