An update on glass fiber dental restorative composites: A systematic review

Dentistry is a much developed field in the last few decades. New techniques have changed the conventional treatment methods as applications of new dental materials give better outcomes. The current century has suddenly forced on dentistry, a new paradigm regarding expected standards for state-of-the-art patient care. Within the field of restorative dentistry, the incredible advances in dental materials research have led to the current availability of esthetic adhesive restorations. The chemistry and structure of the resins and the nature of the glass fiber reinforced systems in dental composites are reviewed in relation to their influence and properties including mechanical, physical, thermal, biocompatibility, technique sensitivity, mode and rate of failure of restorations on clinical application. It is clear that a deeper understanding of the structure of the polymeric matrix and resin-based dental composite is required. As a result of ongoing research in the area of glass fiber reinforced composites and with the development and advancement of these composites, the future prospects of resin-based composite are encouraging. © 2014 Elsevier B.V. All rights reserved

2D and 3D multimodal hybrid face recognition

Abstract. We present a 2D and 3D multimodal hybrid face recognition algorithm and demonstrate its performance on the FRGC v1.0 data. We use hybrid (feature-based and holistic) matching for the 3D faces and a holistic matching approach on the 2D faces. Feature-based matching is performed by offline segmenting each 3D face in the gallery into three regions, namely the eyes-forehead, the nose and the cheeks. The cheeks are discarded to avoid facial expressions and hair. During recognition, each feature in the gallery is automatically matched, using a modified ICP algorithm, with a complete probe face. The holistic 3D and 2D face matching is performed using PCA. Individual matching scores are fused after normalization and the results are compared to the BEE baseline performances in order to provide some answers to the first three conjectures of the FRGC. Our multimodal hybrid algorithm substantially outperformed others by achieving 100 % verification rate at 0.0006 FAR.