Franco Ronchetti

Face recognition based on fuzzy probabilistic SOM

Face recognition is a topic of great interest in different areas, especially those related to security. The identification of a person by the image of her face is a difficult task because of changes experienced by the face due to various factors, such as facial expression, aging and even the lighting. This paper presents a new face recognition technique based on the combination of a competitive fuzzy neural network and a probabilistic decision criterion. The results of this technique on two images database offer satisfactory results. We also discuss the limitations of the proposed technique and future research lines.

Distribution of action movements (DAM): a descriptor for human action recognition

Human action recognition fromskeletal data is an important and active area of research in which the state of the art has not yet achieved near-perfect accuracy on many wellknown datasets. In this paper, we introduce the Distribution of Action Movements Descriptor, a novel action descriptor based on the distribution of the directions of the motions of the joints between frames, over the set of all possible motions in the dataset. The descriptor is computed as a normalized histogram over a set of representative directions of the joints, which are in turn obtained via clustering. While the descriptor is global in the sense that it represents the overall distribution of movement directions of an action, it is able to partially retain its temporal structure by applying a windowing scheme.The descriptor, together with a standard classifier, outperforms several state-of-the-art techniques on many wellknown datasets.

Sign Languague Recognition Without Frame-Sequencing Constraints: A Proof of Concept on the Argentinian Sign Language

Automatic sign language recognition (SLR) is an important topic within the areas of human-computer interaction and machine learning. On the one hand, it poses a complex challenge that requires the intervention of various knowledge areas, such as video processing, image processing, intelligent systems and linguistics. On the other hand, robust recognition of sign language could assist in the translation process and the integration of hearing-impaired people, as well as the teaching of sign language for the hearing population.