No abstract

A spoof or fake is a counterfeit biometric that is used in an attempt to circumvent a biometric sensor. Liveness detection distinguishes between live and fake biometric traits. Liveness detection is based on the principle that additional information can be garnered above and beyond the data procured by a standard verification system, and this additional data can be used to verify if a biometric measure is authentic. The Fingerprint Liveness Detection Competition (LivDet) goal is to compare both software-based (Part 1) and hardware-based (Part 2) fingerprint liveness detection methodologies and is open to all academic and industrial institutions. Submissions for the third edition were much more than in the previous editions of LivDet demonstrating a growing interest in the area. We had nine participants (with eleven algorithms) for Part 1 and two submissions for Part 2.

No abstract

This work copes with the design and implementation of a wireless sensors network architecture to automatically and continuously monitor, for the first time, the manufacturing process of Sardinian Carasau bread. The case of a traditional bakery company facing the challenge of the Food-Industry 4.0 competitiveness is investigated. The process was analyzed to identify the most relevant variables to be monitored during the product manufacturing. Then, a heterogeneous, multi-tier wireless sensors network was designed and realized to allow the real-time control and the data collection during the critical steps of dough production, sheeting, cutting and leavening. Commercial on-the-shelf and cost-effective integrated electronics were employed, making the proposed approach of interest for many practical cases. Finally, a user-friendly interface was provided to enhance the understanding, control and to favor the process monitoring. With the wireless senors network (WSN) we designed, it is possible to monitor environmental parameters (temperature, relative humidity, gas concentrations); cinematic quantities of the belts; and, through a dedicated image processing system, the morphological characteristics of the bread before the baking. The functioning of the WSN was demonstrated and a statistical analysis was performed on the variables monitored during different seasons. offered by Internet-of-Things (IoT) solutions to meet sustainability (or waste reduction) criteria [2]. The food industry is managing critical changes related to consumer needs, to health and safety concerns and to the demand of food products which should be differentiated and high-quality [5]. However, the quality of these products can change suddenly during the production process; thus, leading to the need for ad hoc, reliable and real-time strategies to monitor the manufacturing process [5,6]. Therefore, to satisfy customer demands, the digital monitoring of the supply chain is required to provide a deep knowledge of the crucial production steps, in order to detect the weaknesses of and permit the optimization of the whole process, to reduce the maintenance difficulties and lower the costs [5,6]. Moreover, this digitalization trend in food industry can favor the automatic data collection, the lowering of paperwork and the enabling the development of real-time, robust feedback strategies [2]. Furthermore, the challenge of ensuring acceptable adoption costs of new information and communication technologies (ICT) by small and medium size activities calls for a reasonable and effective answers [4].As a solution to these problems, the use of wireless senors networks (WSNs) was proposed [4]. WSNs are recognized as a relevant technology of the 21st century. A WSN can be defined as a low-cost platform which connects large networks of sensors [7][8][9]. They are systems which comprise radio-frequency (RF) transceivers, sensors, micro-controller or processor and power sources [5,6]. WSNs are a novel and interesting manifestation of the IoT technology [10][11][12]....

A fingerprint presentation attacks detector (FPAD) is designed to obtain a certain performance regardless of the targeted user population. However, two recent works on facial traits showed that a PAD system can exploit very useful information from the targeted user population. In this paper, we explored the existence of that kind of information in fingerprints when textural features are adopted. We show by experiments that such features embed not only intrinsic differences of the given fingerprint replica with respect to a generic live fingerprint, but also contains characteristics present in other fingers of the same user, and characteristics extracted directly from spoofs of the targeted fingerprint itself. These interesting evidences could lead to novel developments in the design of future FPADs