In this paper, we are proposing a 3D segmentation and interactive visualization workflow. The segmentation implementation uses a globally convex multiphase active contours without edges. This algorithm has been proven to be initialization independent due to their globally convex formulation and better than other approaches due to robustness to image variations and adaptive energy functionals. The workflow includes a flexible 3D visualization application that can handle very large volumes using multi-resolution hierarchical data formats following the segmentation. We also designed a custom fragment shader that is capable of meaningfully fusing the data from three different volumes: a segmented label volume, a mean value per voxel volume and a skull striped volume for effective visualization without modifying the segmented results. Giving researchers the access to a whole end to end pipeline, from 3D segmentation to custom real time interactive 3D visualization is, in our opinion, a powerful tool focused on an analyst/expert centric workflow.

Soft biometrics have been emerging to complement other traits and are particularly useful for poor quality data. In this paper, we propose an efficient algorithm to estimate human head poses and to infer soft biometric labels based on the 3D morphology of the human head. Starting by considering a set of pose hypotheses, we use a learning set of head shapes synthesized from anthropometric surveys to derive a set of 3D head centroids that constitutes a metric space. Next, representing queries by sets of 2D head landmarks, we use projective geometry techniques to rank efficiently the joint 3D head centroids/pose hypotheses according to their likelihood of matching each query. The rationale is that the most likely hypotheses are sufficiently close to the query, so a good solution can be found by convex energy minimization techniques. Once a solution has been found, the 3D head centroid and the query are assumed to have similar morphology, yielding the soft label. Our experiments point toward the usefulness of the proposed solution, which can improve the effectiveness of face recognizers and can also be used as a privacy-preserving solution for biometric recognition in public environments.

Sparse representations have been advocated as a relevant advance in biometrics research. In this paper we propose a new algorithm for fusion at the data level of sparse representations, each one obtained from image patches. The main novelties are two-fold: 1) a dictionary fusion scheme is formalised, using the l1--- minimization with the gradient projection method; 2) the proposed representation and classification method does not require the non-overlapping condition of image patches from where individual dictionaries are obtained.