Hand Posture Recognition with Multiview Descriptors

Preservation of asepsis in operating rooms is essential for limiting the contamination of patients by hospital-acquired infections. Strict rules hinder surgeons from interacting directly with any sterile equipement, requiring the intermediary of an assistant or a nurse. Such indirect control may prove itself clumsy and slow up the performed surgery. Gesture-based Human-Computer Interfaces show a promising alternative to assistants and could help surgeons in taking direct control over sterile equipements in the future without jeopardizing asepsis. This paper presents the experiments we led on hand posture feature selection and the obtained results. State-of-the-art description methods classified in four different categories (i.e. local, semi-local, global and geometric description approaches) have been selected to this end. Their recognition rates when combined with a linear Support Vector Machine classifier are compared while attempting to recognize hand postures issued from an ad-hoc database. For each descriptor, we study the effects of removing the background to simulate a segmentation step and the importance of a correct hand framing in the picture. Obtained results show all descriptors benefit to various extents from the segmentation step. Geometric approaches perform best, followed closely by Dalal et al.'s Histogram of Oriented Gradients.