We investigate the use of linear and nonlinear principal manifolds for learning low-dimensional representations for visual recognition. Several leading techniques: Principal Compo...
The performance of face recognition systems that use twodimensional (2D) images is dependent on consistent conditions such as lighting, pose and facial expression. We are developi...
Abstract--Plenty of methods have been proposed in order to discover latent variables (features) in data sets. Such approaches include the principal component analysis (PCA), indepe...
Abstract. In this pilot study, a neural architecture for temporal emotion recognition from image sequences is proposed. The investigation aims at the development of key principles ...
Techniques that can introduce low-dimensional feature representation with enhanced discriminatory power is of paramount importance in face recognition applications. It is well kno...
Juwei Lu, Kostas N. Plataniotis, Anastasios N. Ven...