First International Fingerprint Liveness Detection Competition

Fingerprint recognition systems are vulnerable to artificial spoof fingerprint attacks, like molds made of silicone, gelatin or Play-Doh. “Liveness detection”, which is to detect vitality information from the biometric signature itself, has been proposed to defeat these kinds of spoof attacks. The goal for the LivDet 2009 competition is to compare different methodologies for software-based fingerprint liveness detection with a common experimental protocol and large dataset of spoof and live images. This competition is open to all academic and industrial institutions which have a solution for software-based fingerprint vitality detection problem. Four submissions resulted in successful completion: Dermalog, ATVS, and two anonymous participants (one industrial and one academic). Each participant submitted an algorithm as a Win32 console application. The performance was evaluated for three datasets, from three different optical scanners, each with over 1500 images of “fake” and over 1500 images of “live” fingerprints. The best results were from the algorithm submitted by Dermalog with a performance of 2.7% FRR and 2.8% FAR for the Identix (L-1) dataset. The competition goal is to become a reference event for academic and industrial research in software-based fingerprint liveness detection and to raise the visibility of this important research area in order to decrease risk of fingerprint systems to spoof attacks

Sleep detection using physiological signals from a wearable device

Internet of things for medical devices is revolutionizing healthcare industry by providing platforms for data collection via cloud gateways and analytics. In this paper, we propose a process for developing a proof of concept solution for sleep detection by observing a set of am- bulatory physiological parameters in a completely non-invasive manner. Observing and detecting the state of sleep and also its quality, in an objective way, has been a challenging problem that impacts many medical fields. With the solution presented here, we propose to collect physiological signals from wearable devices, which in our case consists of a smart wristband equipped with sensors and a protocol for communication with a mobile device. With machine learning based algorithms, that we developed, we are able to detect sleep from wakefulness in up to 93% of cases. The results from our study are promising with a potential for novel insights and effective methods to manage sleep disturbances and improve sleep quality

Sleep Pose Recognition in an ICU Using Multimodal Data and Environmental Feedback

Clinical evidence suggests that sleep pose analysis can shed light onto patient recovery rates and responses to therapies. In this work, we introduce a formulation that combines features from multimodal data to classify human sleep poses in an Intensive Care Unit (ICU) environment. As opposed to the current methods that combine data from multiple sensors to generate a single feature, we extract features independently. We then use these features to estimate candidate labels and infer a pose. Our method uses modality trusts–each modality’s classification ability–to handle variable scene conditions and to deal with sensor malfunctions. Specifically, we exploit shape and appearance features extracted from three sensor modalities: RGB, depth, and pressure. Classification results indicate that our method achieves 100% accuracy (outperforming previous techniques by 6%) in bright and clear (ideal) scenes, 70% in poorly illuminated scenes, and 90% in occluded ones

Advanced signal processing algorithms for cardiorespiratory monitoring in the neonatal intensive care unit

This chapter provides basic principles aimed at understanding advanced engineering and mathematical methods that could potentially provide effective assisting technology in the neonatal intensive care unit (NICU). The included material by no means represents a comprehensive review of the state-of-the-art in the field, but purposely travels along a very narrow line in order to offer the reader a specific pathway needed to understand some of the main conceptual and practical steps to be considered from a biomedical engineering point of view. The chapter is organized in compact paragraphs aimed at threading lines guiding the reader through a narrowly selected bibliographic body of knowledge. The selected material is divided in four sections. The first three sections concisely outline the three main research lines and connect the reader to Clinical, Physiological, and Methodological Background respectively. The last fourth section provides a methodological outline of a specific exemplary approach based on a physiological model of heartbeat dynamics defined by using the statistical theory of point processes