No abstract

Compton imaging devices have been proposed and studied for a wide range of applications. We have developed a Compton camera prototype which can be operated with two or three detector layers based on monolithic lanthanum bromide ([Formula: see text]) crystals coupled to silicon photomultipliers (SiPMs), to be used for proton range verification in hadron therapy. In this work, we present the results obtained with our prototype in laboratory tests with radioactive sources and in simulation studies. Images of a [Formula: see text]Na and an [Formula: see text]Y radioactive sources have been successfully reconstructed. The full width half maximum of the reconstructed images is below 4 mm for a [Formula: see text]Na source at a distance of 5 cm.

The increasing demand for traveler clearance at international border crossing points (BCPs) has motivated research for finding more efficient solutions. Automated border control (ABC) is emerging as a solution to enhance the convenience of travelers, the throughput of BCPs, and national security. This is the first comprehensive survey on the biometric techniques and systems that enable automatic identity verification in ABC. We survey the biometric literature relevant to identity verification and summarize the best practices and biometric techniques applicable to ABC, relying on a real experience collected in the field. Furthermore, we select some of the major biometric issues raised and highlight the open research areas.

We introduce a system that processes a sequence of images of a front-facing human face and recognises a set of facial expressions. We use an efficient appearance-based face tracker to locate the face in the image sequence and estimate the deformation of its non-rigid components. The tracker works in real-time. It is robust to strong illumination changes and factors out changes in appearance caused by illumination from changes due to face deformation. We adopt a model-based approach for facial expression recognition. In our model, an image of a face is represented by a point in a deformation space. The variability of the classes of images associated to facial expressions are represented by a set of samples which model a low-dimensional manifold in the space of deformations. We introduce a probabilistic procedure based on a nearest-neighbour approach to combine the information provided by the incoming image sequence with the prior information stored in the expression manifold in order to compute a posterior probability associated to a facial expression. In the experiments conducted we show that this system is able to work in an unconstrained environment with strong changes in illumination and face location. It achieves an 89% recognition rate in a set of 333 sequences from the Cohn-Kanade data base.