Automatic classification of facial morphology for medical applications

Facial morphology measurement and classification play important role in the face anthropometry of many medical applications. This usually involves the investigation of medical abnormalities where specific facial features are studied by taking a number of measurements of the facial area under investigation. The measurements are often obtained from the three-dimensional (3D) scans of the faces; however, the measurements are often made manually, which is tedious and time consuming process. Moreover, in gene related studies thousands of measurements may be necessary in order to find statistically significant relationships between facial features and genes. Normative studies, from which typical populous models can be built, also require many measurements. Thus an automatic method to extract morphological measurements and interpret them is desirable. In this article, an automatic method for classification of facial morphology on the basis of a number of geometric measurements obtained automatically from 3D facial scans is presented. Among different facial features the philtrum, which is the vertical groove extending from the nose to the upper lip and the lip area, plays an important role in defining the interaction between the genes and craniofacial anomalies such as, for example, cleft lip and palate. In this paper, geometric features are analysed for their suitability to classify philtrum into three classes previously proposed by medical experts. Moreover, further analysis is conducted to assess the best number of classes to model the underlying data distribution from the point of view of classification accuracy. The obtained classification results are compared with the ground truth manual labelling of 3D face meshes provided by a medical expert. The dataset used for this research is taken from ALSPAC dataset and consists of 1000 3D face meshes. The proposed method achieves classification accuracy of 97% for this data set using the Mean, Minimum and Maximum curvature features in combination

FROM THE ELABORATION PROCESS OF POINT CLOUD TO INFORMATION SYSTEMS BOTH FOR PLANNING AND DESIGN MANAGEMENT OF CULTURAL HERITAGE

Abstract. Nowadays we are able to produce geometric models of historical building at different scale of detail using photos and measurements. More and more we are facing with lack of preservation actions and maintenance activities, bad foreseen policies, unexpected natural events, that are forcing professionals and researchers to operate without usual data. In these cases, we need consistent repository to collect and distribute data to produce information. Furthermore, we need to "give intelligence" to these repositories in order to query them with respect geometrical instances, topological issues, historical features.We dispose of tons of xyz points: how can we pass from the point cloud to a building information model, then to a geographic information system, not necessarily in this order? A simple Scan-to-BIM-to-GIS and Scan-to-GIS-to-BIM process were tested in order to consequently evaluate, with purposes of preservation and of enhancing of resilience, some practices that could became the best, also in terms of time and cost saving.The work we propose is a part of an ongoing research focused on the application of H-BIM approach for the management of historical building heritage, focused on a district management (H-DIM, at an urban level). In particular, with regard to the resilience theme, both the acquisition phase and the archive research process are of great importance for protecting our undefended building heritage.Regarding the case study of the paper, UNESCO sites represent important areas for collective interests of humanity. This contribution proposes a possible solution applying a digital cultural heritage to the historical part of the Municipality of Serralunga d'Alba belonging to the UNESCO site called Vineyard Landscape of Langhe-Roero and Monferrato.</p

Automatic 3D face recognition combining global geometric features with local shape variation information

Face recognition is a focused issue in pattern recognition over the past decades. In this paper, we have proposed a new scheme for face recognition using 3D information. In this scheme, the scattered 3D point cloud is first represented with a regular mesh using hierarchical mesh fitting. Then the local shape variation information is extracted to characterize the individual together with the global geometric features. Experimental results on 3D_RMA, a likely largest 3D face database available currently, demonstrate that the local shape variation information is very important to improve the recognition accuracy and that the proposed algorithm has promising performance with a low computational cost