Automated border control systems: biometric challenges and research trends

Automated Border Control (ABC) systems automatically verify the travelers\u2019 identity using their biometric information, without the need of a manual check, by comparing the data stored in the electronic document (e.g., the e-Passport) with a live sample captured during the crossing of the border. In this paper, the hardware and software components of the biometric systems used in ABC systems are described, along with the latest challenges and research trends

Advanced design of Automated Border Control gates: biometric system techniques and research trends

Last few years have witnessed an ever-increasing demand of border crossing, whose processing introduces the need to speed-up the clearance process at the Border Crossing Points (BCP). Automated Border Control (ABC) gates, or shortly e-Gates, can verify the identity of the travelers crossing the borders by exploiting their biometric traits, without the need of a constant human intervention. Biometric technologies have a relevant impact on the improvement of efficiency, effectiveness and security of the checking processes. Automated biometric recognition can increase the border processing throughput of the BCP, as well as facilitate the clearance procedures. To grant the passage of the border, the e-Gate compares the biometric samples of the traveler stored into the electronic document with live acquisitions. This paper presents the latest substantial advances in the design of e-Gates. In particular, it presents the Biometric Verification System in detail, including its hardware and software components, as well as the procedures followed during the biometric verification of the traveler's identity. We address the complex issue of measuring the performance of an ABC system, considering the real applicability of the figures of merit usually adopted in biometric system's evaluation. To complete the view of the current e-Gates, we highlight the main challenges and the research trends relating to the biometric systems currently used in e-Gates

Analyzing images in frequency domain to estimate the quality of wood particles in OSB production

The analysis of the quality of particulate materials is of great importance for a variety of research and industrial applications. Most image-based methods rely on the segmentation of the image to measure the particles and aggregate their characteristics. However, the segmentation of particulate materials can be severely affected when the setup is not controlled. For instance, when there are device errors, changes in the light conditions, or when the camera gets dirty because of the dust or a similar substance. All of these circumstances are common in industrial setups, like the one studied in this paper. This work presents a framework for quality estimation based on image processing algorithms that avoids segmentation. The considered application scenario is the online quality control of the production of Oriented Strand Boards (OSB), a type of wood panel frequently used in construction and manufacturing industries. The proposed method quantizes frequency domain into a histogram using a non-parametric method, which is later exploited using computational intelligence to classify the quality of superimposed wood particles deposed on a conveyor belt. The method has been tested using synthetic and real images with different noise conditions. The results illustrate the robustness of the approach and its capability to detect significant quality changes in the wood particles

Improving OSB wood panel production by vision-based systems for granulometric estimation

Oriented Strand Board (OSB) is a kind of engineered wood particle board widely adopted in manufacturing, construction and logistics. The production of OSB panels requires rectangular-shaped wood strands of specific size, arranged in layers to form the so-called \u201cmattress\u201d (mat) and bonded together with glue. The structural properties of the panel rely directly on the layer forming. In particular, the size distribution - namely granulometry - of the strands should fulfill standard measures to reach the mechanical properties expected from the panel. Offline granulometry of particle materials is the most commonly procedure used to evaluate the production process, but it is prone to several drawbacks owing to the manual intervention of human operators. Vision-based systems, instead, allow for performing granulometric analyses in an automatic and contactless manner. We propose a computer vision approach to estimate the granulometry of wood strands. The designed framework analyzes images of a mat of strands placed over a moving conveyor belt, and provides useful information and measurements to enhance the production of OSB panels. Because of the very large amount of wood strands on the mat, particle-overlapping is frequent and represents a main issue for measurement algorithms. In order to overcome this problem, our framework joins image processing and computational intelligence methods, such as edge detection and fuzzy color clustering. We tested the framework with real and synthetic images, useful to variate the conditions of the material's shape. The obtained results demonstrate the ability of our approach to evaluate the granulometry of the strands in real conditions, and robustness against the simulated variations of the production process

Automatic classification of acquisition problems affecting fingerprint images in automated border controls

Automated Border Control (ABC) systems are technologies designed to increase the speed and accuracy of the identity verifications performed at international borders. A great number of ABCs deployed in different countries use fingerprint recognition techniques because of their high accuracy and user acceptability. However, the accuracy of fingerprint recognition methods can drastically decrease in this application context due to user-sensor interaction factors. This paper presents two main contributions. The first of them consists in an experimental evaluation performed to search the main negative aspects that could affect the usability and accuracy in ABCs based on fingerprint biometrics. The mainly considered aspects consists in the presence of luggage and cleanness of the finger skin. The second contribution consists in a novel approach for automatically identifying the type of user-sensor interaction that caused quality degradations in fingerprint samples. This method uses a specific feature set and computational intelligence techniques to detect non-idealities in the acquisition process and to suggest corrective actions to travelers and border guards. To the best of our knowledge, this is the first method in the literature designed to detect problems in user-sensor interaction different from improper pressures on the acquisition surface. We validated the proposed approach using a dataset of 2880 images simulating different scenarios typical of ABCs. Results shown that the proposed approach is feasible and can obtain satisfactory performance, with a classification error of 0.098

Age Estimation Based on Face Images and Pre-trained Convolutional Neural Networks

Age estimation based on face images plays an important role in a wide range of scenarios, including security and defense applications, border control, human-machine interaction in ambient intelligence applications, and recognition based on soft biometric information. Recent methods based on deep learning have shown promising performance in this field. Most of these methods use deep networks specifically designed and trained to cope with this problem. There are also some studies that focus on applying deep networks pre-trained for face recognition, which perform a fine-tuning to achieve accurate results. Differently, in this paper, we propose a preliminary study on increasing the performance of pre-trained deep networks by applying postprocessing strategies. The main advantage with respect to finetuning strategies consists of the simplicity and low computational cost of the post-processing step. To the best of our knowledge, this paper is the first study on age estimation that proposes the use of post-processing strategies for features extracted using pretrained deep networks. Our method exploits a set of pre-trained Convolutional Neural Networks (CNNs) to extract features from the input face image. The method then performs a feature level fusion, reduces the dimensionality of the feature space, and estimates the age of the individual by using a Feed-Forward Neural Network (FFNN). We evaluated the performance of our method on a public dataset (Adience Benchmark of Unfiltered Faces for Gender and Age Classification) and on a dataset of nonideal samples affected by controlled rotations, which we collected in our laboratory. Our age estimation method obtained better or comparable results with respect to state-of-the-art techniques and achieved satisfactory performance in non-ideal conditions. Results also showed that CNNs trained on general datasets can obtain satisfactory accuracy for different types of validation images, also without applying fine-tuning methods

Enhancing the performance of multimodal automated border control systems

Biometric recognition in Automated Border Control (ABC) systems is performed in response to an increased worldwide traffic, by automatically verifying the identity of the passenger during border crossing. Currently, ABC systems seldom use methods for multimodal biometric fusion, which have been proved to increase the recognition accuracy, due to technological and privacy limitations. This paper proposes a framework for the biometric fusion in ABC systems, with the features of being technology-neutral and privacy- compliant, by performing an analysis of the most suitable biometric fusion techniques for ABC systems and considering the current technical and legal limitations

An optical transmission spectrum for the ultra-hot Jupiter WASP-121b measured with the Hubble Space Telescope

This is the author accepted manuscript. The final version is available from American Astronomical Society / IOP Publishing via the DOI in this record.We present an atmospheric transmission spectrum for the ultra-hot Jupiter WASP-121b, measured using the Space Telescope Imaging Spectrograph (STIS) onboard the Hubble Space Telescope (HST). Across the 0.47-1 micron wavelength range, the data imply an atmospheric opacity comparable to - and in some spectroscopic channels exceeding - that previously measured at near-infrared wavelengths (1.15-1.65 micron). Wavelength-dependent variations in the opacity rule out a gray cloud deck at a confidence level of 3.8-sigma and may instead be explained by VO spectral bands. We find a cloud-free model assuming chemical equilibrium for a temperature of 1500K and metal enrichment of 10-30x solar matches these data well. Using a free-chemistry retrieval analysis, we estimate a VO abundance of -6.6(-0.3,+0.2) dex. We find no evidence for TiO and place a 3-sigma upper limit of -7.9 dex on its abundance, suggesting TiO may have condensed from the gas phase at the day-night limb. The opacity rises steeply at the shortest wavelengths, increasing by approximately five pressure scale heights from 0.47 to 0.3 micron in wavelength. If this feature is caused by Rayleigh scattering due to uniformly-distributed aerosols, it would imply an unphysically high temperature of 6810+/-1530K. One alternative explanation for the short-wavelength rise is absorption due to SH (mercapto radical), which has been predicted as an important product of non-equilibrium chemistry in hot Jupiter atmospheres. Irrespective of the identity of the NUV absorber, it likely captures a significant amount of incident stellar radiation at low pressures, thus playing a significant role in the overall energy budget, thermal structure, and circulation of the atmosphere.Support for program GO-14767 was provided by NASA through a grant from the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Inc., under NASA contract NAS 5-26555. T.M.E., D.K.S., and N.N. acknowledge funding from the European Research Council under the European Unions Seventh Framework Programme (FP7/2007-2013)/ERC grant agreement no. 336792. G.W.H. and M.H.W. acknowledge support from Tennessee State University and the State of Tennessee through its Centers of Excellence program. J.S.F. acknowledges funding by the Spanish MINECO grant AYA2016-79425-C3-2-P. J.K.B. is supported by a Royal Astronomical Society Research Fellowship. This work has been carried out in the frame of the National Centre for Competence in Research PlanetS supported by the Swiss National Science Foundation (SNSF). V.B. and D.E. have received funding from the European Research Council (ERC) under the European Unions Horizon 2020 research and innovation programme (project Four Aces; grant agreement no. 724427)

The Next White (NEW) detector

[EN] Conceived to host 5 kg of xenón at a pressure of 15 bar in the ¿ducial volume,the NEXTWhite (NEW)apparatus is currently the largest high pressure xenon gas TPC using electroluminescent ampli¿cation in the world. It is also a 1:2 scale model of the NEXT-100 detector scheduled to start searching for ßß0¿ decays in 136Xe in 2019. Both detectors measure the energy of the event using a plane of photomultipliers located behind a transparent cathode. They can also reconstruct the trajectories of charged tracks in the dense gas of the TPC with the help of a plane of silicon photomultipliers located behind the anode. A sophisticated gas system, common to both detectors, allows the high gas purity needed to guarantee a long electron lifetime. NEXT-White has been operating since October 2017 at the Canfranc Underground Laboratory (LSC), in Spain. This paper describes the detector and associated infrastructures.The NEXT Collaboration acknowledges support from the following agencies and institutions: the European Research Council (ERC) under the Advanced Grant 339787-NEXT; the European Union's Framework Programme for Research and Innovation Horizon 2020 (2014-2020) under the Marie Sklodowska-Curie Grant Agreements No. 674896, 690575 and 740055; the Ministerio de Economia y Competitividad of Spain under grants FIS2014-53371-C04, the Severo Ochoa Program SEV-2014-0398 and the Maria de Maetzu Program MDM-2016-0692; the GVA of Spain under grants PROMETEO/2016/120 and SEJI/2017/011; the Portuguese FCT and FEDER through the program COMPETE, projects PTDC/FIS-NUC/2525/2014 and UID/FIS/04559/2013; the U.S. Department of Energy under contract numbers DE-AC02-07CH11359 (Fermi National Accelerator Laboratory), DE-FG02-13ER42020 (Texas A&M), DE-SC0017721 (University of Texas at Arlington), and DE-AC02-06CH11357 (Argonne National Laboratory); and the University of Texas at Arlington. 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