John Soldera

TL;DR: This work describes a model for understanding people motion in video sequences using Voronoi diagrams, focusing on group detection and classification, and determines the temporal evolution of some sociological and psychological parameters that are used to compute individual characteristics.

TL;DR: A novel face recognition method based on projections of high-dimensional face image representations into lower dimensionality and highly discriminative spaces is proposed by a modified orthogonal locality preserving projection (OLPP) method that uses a customized locality definition scheme to preserve the face class structure in the lowerdimensionality face feature space.

TL;DR: Experimental results showed that different criteria for detecting unusual motion in surveillance cameras can be used as an automatic pre-screening of suspect motion in Surveillance applications.

TL;DR: A novel generative approach for face recognition is proposed, in which sparse facial features are extracted from high resolution color face images using predefined landmark topologies which mark discriminative locations on face images, unlike the appearance-based approach, which low resolution grayscale face images are used, reducing the computational complexity.

TL;DR: The proposed face recognition method was compared to methods representative of the state-of-the-art, using color or grayscale face images, and presented higher recognition rates and also is efficient in general texture discrimination (e.g., texture recognition of material images), as the experiments suggest.