Testing the utility of a data-driven approach for assessing BMI from face images

Several lines of evidence suggest that facial cues of adiposity may be important for human social interaction. However, tests for quantifiable cues of body mass index (BMI) in the face have examined only a small number of facial proportions and these proportions were found to have relatively low predictive power. Here we employed a data-driven approach in which statistical models were built using principal components (PCs) derived from objectively defined shape and color characteristics in face images. The predictive power of these models was then compared with models based on previously studied facial proportions (perimeter-to-area ratio, width-to-height ratio, and cheek-to-jaw width). Models based on 2D shape-only PCs, color-only PCs, and 2D shape and color PCs combined each performed significantly and substantially better than models based on one or more of the previously studied facial proportions. A non-linear PC model considering both 2D shape and color PCs was the best predictor of BMI. These results highlight the utility of a “bottom-up”, data-driven approach for assessing BMI from face images

Semantically Informed Multiview Surface Refinement

We present a method to jointly refine the geometry and semantic segmentation of 3D surface meshes. Our method alternates between updating the shape and the semantic labels. In the geometry refinement step, the mesh is deformed with variational energy minimization, such that it simultaneously maximizes photo-consistency and the compatibility of the semantic segmentations across a set of calibrated images. Label-specific shape priors account for interactions between the geometry and the semantic labels in 3D. In the semantic segmentation step, the labels on the mesh are updated with MRF inference, such that they are compatible with the semantic segmentations in the input images. Also, this step includes prior assumptions about the surface shape of different semantic classes. The priors induce a tight coupling, where semantic information influences the shape update and vice versa. Specifically, we introduce priors that favor (i) adaptive smoothing, depending on the class label; (ii) straightness of class boundaries; and (iii) semantic labels that are consistent with the surface orientation. The novel mesh-based reconstruction is evaluated in a series of experiments with real and synthetic data. We compare both to state-of-the-art, voxel-based semantic 3D reconstruction, and to purely geometric mesh refinement, and demonstrate that the proposed scheme yields improved 3D geometry as well as an improved semantic segmentation

Integration between calibrated time-of-flight camera data and multi-image matching approach for architectural survey

In this work, the integration between data provided by Time-of-Flight cameras and a multi-image matching technique for metric surveys of architectural elements is presented. The main advantage is given by the quickness in the data acquisition (few minutes) and the reduced cost of the instruments. The goal of this approach is the automatic extraction of the object breaklines in a 3D environment using a photogrammetric process, which is helpful for the final user exigencies for the reduction of the time needed for the drawing production. The results of the performed tests on some architectural elements will be reported in this paper

Documentation and analysis of plastic fingerprint impressions involving contactless three-dimensional surface scanning

Fingerprint impressions are frequently encountered during the investigation of crime scenes, and may establish a crucial linkage between the suspect and the crime scene. Plastic fingerprint impressions found at crime scenes are often transient and delicate, leaving photography the sole means of documentation. A traditional photography approach can be inadequate in documenting impressions that contain three-dimensional (3D) details due to the limitations of camera and lighting conditions on scene. In this study, 3D scanning was proposed as a novel method for the documentation of plastic fingerprints. Structured-light 3D scanning (SLS) captures the distortion of projected light patterns on the subject to obtain its 3D profile, which allows fast acquisition of the complete 3D geometric information of the surface. The contactless operation of SLS also eliminates the risk of destroying fragile evidence, making it a sound choice for forensic applications. In this study, the feasibility of 3D scanning of plastic fingerprint impressions was evaluated and compared with traditional photography regarding the quantity and quality of perceptible friction ridge features. Attempts were made to develop a procedure to extract curvature features from 3D scanned fingerprints and flatten the friction ridge features into two-dimensional (2D) images to allow direct comparison with the traditional photography method in the CSIpix® Matcher and NFIQ 2.0 software. One of the developed methods (3DR) utilizing a discrete geometry operator and convexity features outperformed traditional photography, both in minutiae count and match quality, while traditional photography could not always capture enough high-quality minutiae for comparisons, even after digital enhancement. The reproducibility of the 3D scanning process was evaluated using 3D point cloud statistics. The pair-wise mean distance and standard deviation were calculated for four levels of comparisons with theoretically increasing disparity, including pairs of scans of the same impressions. The results showed minimal shape deviation from scan to scan for the same impression, but high variations for different impressions