Six basic facial expressions are investigated when the human face is partially occluded, i.e. when the eyes and eyebrows or the mouth regions are occluded. Such occlusions occur when a person wears glasses (e.g. in VR application) or a mouth mask (e.g. in medical application). More specifically, we are interested in finding the part of the face that contains sufficient information in order to correctly classify these six expressions. Two facial image

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

In this paper 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

Human facial expression analysis and synthesis play a central role in the social context, being investigated by many psychologists over the time. Moreover, an increased interest has been shown in developing an automated facial expression analyzer capable of recognizing, classifying and then synthesizing human expressions on synthetic “talking heads”. This paper surveys the state of the art in this area.

Two hybrid systems for classifying seven categories of human facial expression are proposed. The first system combines independent component analysis (ICA) and support vector machines (SVMs). The original face image database is decomposed into linear combinations of several basis images, where the corresponding coefficients of these combinations are fed up into SVMs instead of an original feature vector comprised of grayscale image pixel values.

In this paper we explore the independent component decomposition for face detection. The minimization of the Kullback - Leibler divergence and the maximization of the entropy are two methods employed to decompose an original image into its independent components. We built nearest neighbor classifiers based on their resulting independent components and compare their ability to detect faces to that of support vector machines.

The paper proposes the application of majority voting on the output of several support vector machines in order to select the most suitable learning machine for frontal face detection. The first experimental results indicate a significant reduction of the rate of false positive patterns.

The so-called support vector machine (SVM) is a special kind of the learning machines, whose idea was given by Vapnik. The learning capability of the support vector machines depends on the Vapnik-Chervonenkis- (VC) dimension of the kernel function of used. In this paper we are going to construct a new kernel function, which is based on Walsh functions, for support vector machines. We are proving some theoretical results about the VC-dimension of