This paper addresses the problem of face presentation attack detection using different image modalities. In particular, the usage of short wave infrared (SWIR) imaging is considered. Face presentation attack detection is performed using recent models based on Convolutional Neural Networks using only carefully selected SWIR image differences as input. Conducted experiments show superior performance over similar models acting on either color images or on a combination of different modalities (visible, NIR, thermal and depth), as well as on a SVM-based classifier acting on SWIR image differences. Experiments have been carried on a new public and freely available database, containing a wide variety of attacks. Video sequences have been recorded thanks to several sensors resulting in 14 different streams in the visible, NIR, SWIR and thermal spectra, as well as depth data. The best proposed approach is able to almost perfectly detect all impersonation attacks while ensuring low classification errors. On the other hand, obtained results show that obfuscation attacks are more difficult to detect. We hope that the proposed database will foster research on this challenging problem. Finally, all the code and instructions to reproduce presented experiments is made available to the research community.

In this work, we present a general framework for building a biometrics system capable of capturing multispectral data from a series of sensors synchronized with active illumination sources. The framework unifies the system design for different biometric modalities and its realization on face, finger and iris data is described in detail. To the best of our knowledge, the presented design is the first to employ such a diverse set of electromagnetic spectrum bands, ranging from visible to long-wave-infrared wavelengths, and is capable of acquiring large volumes of data in seconds, which enabled us to successfully conduct a series of data collection events. We also present a comprehensive analysis on the captured data using a deep-learning classifier for presentation attack detection. Our analysis follows a data-centric approach attempting to highlight the strengths and weaknesses of each spectral band at distinguishing live from fake samples.

Current finger-vein or palm-vein recognition systems usually require direct contact of the subject with the apparatus. This can be problematic in environments where hygiene is of primary importance. In this work we present a contactless vascular biometrics sensor platform named sweet which can be used for hand vascular biometrics studies (wrist, palm, and finger-vein) and surface features such as palmprint. It supports several acquisition modalities such as multi-spectral Near-Infrared (NIR), RGB-color, Stereo Vision (SV) and Photometric Stereo (PS). Using this platform we collected a dataset consisting of the fingers, palm and wrist vascular data of 120 subjects. We present biometric experimental results, focusing on Finger-Vein Recognition (FVR). Finally, we discuss fusion of multiple modalities. The acquisition software, parts of the hardware design, the new FV dataset, as well as source-code for our experiments are publicly available for research purposes.

It is urgent that the built environment transitions towards renewable energies and adopts ambitious renovations strategies as both are unavoidable steps towards long term sustainability goals. While extensive research has been focused on the integration of buildings in renewable energy production networks as well as on renovation measures such as thermal insulation and installation of heat-pumps, it is still unclear how these actions perform at scale when combined together. To fill this gap, this paper introduces the digital platform Open-data for Building Energy Efficiency, Renovation and Storage (OpenBEERS) that allows simulation of renewable energy deployment scenarios at the urban scale to help municipalities and energy professionals plan transition strategies effectively. The platform combines the state-of-the-art CitySim physics-based solver with the BASOPRA optimization framework allowing us to assess the techno-economic feasibility of distributed energy resources (DERs), including heat pumps and electric vehicles (EVs). Out tool integrates a 3D interface for the visualization of the input and output data. Our approach is validated with three case studies, municipalities in the State of Valais where a detailed analysis of DERs deployment scenarios is conducted.