A PNU-Based Methodology to Improve the Reliability of Biometric Systems.

Face recognition is an important application of pattern recognition and image analysis in biometric security systems. The COVID-19 outbreak has introduced several issues that can negatively affect the reliability of the facial recognition systems currently available: on the one hand, wearing a face mask/covering has led to growth in failure cases, while on the other, the restrictions on direct contact between people can prevent any biometric data being acquired in controlled environments. To effectively address these issues, we designed a hybrid methodology that improves the reliability of facial recognition systems. A well-known Source Camera Identification (SCI) technique, based on Pixel Non-Uniformity (PNU), was applied to analyze the integrity of the input video stream as well as to detect any tampered/fake frames. To examine the behavior of this methodology in real-life use cases, we implemented a prototype that showed two novel properties compared to the current state-of-the-art of biometric systems: (a) high accuracy even when subjects are wearing a face mask; (b) whenever the input video is produced by deep fake techniques (replacing the face of the main subject) the system can recognize that it has been altered providing more than one alert message. This methodology proved not only to be simultaneously more robust to mask induced occlusions but also even more reliable in preventing forgery attacks on the input video stream.

A pervasive visual-haptic framework for virtual delivery training.

Thanks to the advances of voltage regulator (VR) technologies and haptic systems, virtual simulators are increasingly becoming a viable alternative to physical simulators in medicine and surgery, though many challenges still remain. In this study, a pervasive visual-haptic framework aimed to the training of obstetricians and midwives to vaginal delivery is described. The haptic feedback is provided by means of two hand-based haptic devices able to reproduce force-feedbacks on fingers and arms, thus enabling a much more realistic manipulation respect to stylus-based solutions. The interactive simulation is not solely driven by an approximated model of complex forces and physical constraints but, instead, is approached by a formal modeling of the whole labor and of the assistance/intervention procedures performed by means of a timed automata network and applied to a parametrical 3-D model of the anatomy, able to mimic a wide range of configurations. This novel methodology is able to represent not only the sequence of the main events associated to either a spontaneous or to an operative childbirth process, but also to help in validating the manual intervention as the actions performed by the user during the simulation are evaluated according to established medical guidelines. A discussion on the first results as well as on the challenges still unaddressed is included.