Showing papers by "Vincenzo Piuri published in 2016"
TL;DR: The biometric literature relevant to identity verification is surveyed and the best practices and biometric techniques applicable to ABC are summarized, relying on real experience collected in the field.
Abstract: The increasing demand for traveler clearance at international border crossing points (BCPs) has motivated research for finding more efficient solutions. Automated border control (ABC) is emerging as a solution to enhance the convenience of travelers, the throughput of BCPs, and national security. This is the first comprehensive survey on the biometric techniques and systems that enable automatic identity verification in ABC. We survey the biometric literature relevant to identity verification and summarize the best practices and biometric techniques applicable to ABC, relying on real experience collected in the field. Furthermore, we select some of the major biometric issues raised and highlight the open research areas.
72 citations
01 Feb 2016
TL;DR: The proposed fully touchless fingerprint recognition system adopts an innovative and less-constrained acquisition setup, does not require contact with any surface or a finger placement guide, and simultaneously captures multiple images while the finger is moving, and proposes novel algorithms for computing 3-D models of the shape of a finger.
Abstract: Touchless fingerprint recognition systems do not require contact of the finger with any acquisition surface and thus provide an increased level of hygiene, usability, and user acceptability of fingerprint-based biometric technologies. The most accurate touchless approaches compute 3-D models of the fingertip. However, a relevant drawback of these systems is that they usually require constrained and highly cooperative acquisition methods. We present a novel, fully touchless fingerprint recognition system based on the computation of 3-D models. It adopts an innovative and less-constrained acquisition setup compared with other previously reported 3-D systems, does not require contact with any surface or a finger placement guide, and simultaneously captures multiple images while the finger is moving. To compensate for possible differences in finger placement, we propose novel algorithms for computing 3-D models of the shape of a finger. Moreover, we present a new matching strategy based on the computation of multiple touch-compatible images. We evaluated different aspects of the biometric system: acceptability, usability, recognition performance, robustness to environmental conditions and finger misplacements, and compatibility and interoperability with touch-based technologies. The proposed system proved to be more acceptable and usable than touch-based techniques. Moreover, the system displayed satisfactory accuracy, achieving an equal error rate of 0.06% on a dataset of 2368 samples acquired in a single session and 0.22% on a dataset of 2368 samples acquired over the course of one year. The system was also robust to environmental conditions and to a wide range of finger rotations. The compatibility and interoperability with touch-based technologies was greater or comparable to those reported in public tests using commercial touchless devices.
67 citations
01 Jul 2016
TL;DR: This paper proposes the first innovative method in the literature able to extract Level 3 features, in particular sweat pores, from fingerprint images captured with a touchless acquisition using a commercial off-the-shelf camera.
Abstract: Touchless fingerprint recognition systems are being increasingly used for a fast, hygienic, and distortion-free recognition. However, due to the greater complexity of the algorithms required for processing touchless fingerprint samples, currently only Level 1 and Level 2 features are being used for recognition, and Level 3 features are used only in touch-based optical devices with about 1000 ppi resolution. In this paper, we propose the first innovative method in the literature able to extract Level 3 features, in particular sweat pores, from fingerprint images captured with a touchless acquisition using a commercial off-the-shelf camera. The method uses image processing algorithms to extract a set of candidate sweat pores. Then, computational intelligence techniques based on neural networks are used to learn the local features of the real pores, and select only the actual sweat pores from the set of candidate points. The results show the validity of the proposed methodology, with the majority of the pores correctly extracted, indicating that a touchless fingerprint recognition using Level 3 features is feasible.
24 citations
01 Mar 2016
TL;DR: In this paper, the authors proposed a new control strategy based on the combination of the adaptive and optimal control by applying time-sharing in the Sigmoid Generated Fixed Point Transformation (SGFPT) method.
Abstract: With the aim of evading the difficulties of the Lyapunov function-based techniques in the control of nonlinear systems recently the Sigmoid Generated Fixed Point Transformation (SGFPT) has been introduced. This systematic method has been presented for the generation of whole families of Fixed Point Transformations that can be used in nonlinear adaptive control of Single Input Single Output (SISO) as well as Multiple Input Multiple Output (MIMO) systems. This paper proposes a new control strategy based on the combination of the adaptive and optimal control by applying time-sharing in the SGFPT method. The scheduling strategy supports error containment by cyclic control of the different variables. Further, this paper introduces new improvements on SGFPT technique by introducing Stretched Sigmoid Functions. The efficiency of the presented control solution has been applied in the adaptive control of an underactuated mechanical system. Simulation results validate that the proposed scheme is far promising.
16 citations
17 Nov 2016
TL;DR: This paper proposes a novel approach for supporting application requirements (typically related to security, due to the inevitable concerns arising whenever data are stored and managed at external third parties) in cloud-based IoT data processing.
Abstract: IoT infrastructures can be seen as an interconnected network of sources of data, whose analysis and processing can be beneficial for our society. Since IoT devices are limited in storage and computation capabilities, relying on external cloud providers has recently been identified as a promising solution for storing and managing IoT data. Due to the heterogeneity of IoT data and applicative scenarios, the cloud service delivery should be driven by the requirements of the specific IoT applications. In this paper, we propose a novel approach for supporting application requirements (typically related to security, due to the inevitable concerns arising whenever data are stored and managed at external third parties) in cloud-based IoT data processing. Our solution allows a subject with an authority over an IoT infrastructure to formulate conditions that the provider must satisfy in service provisioning, and computes a SLA based on these conditions while accounting for possible dependencies among them. We also illustrate a CSP-based formulation of the problem of computing a SLA, which can be solved adopting off-the-shelves CSP solvers.
16 citations
TL;DR: This work presents computational intelligence techniques applied to biometrics, from both a theoretical and an application point of view.
Abstract: Biometric systems consist of devices, procedures, and algorithms used to recognize people based on their physiological or behavioral features, known as biometric traits. Computational intelligence (CI) approaches are widely adopted in establishing identity based on biometrics and also to overcome non-idealities typically present in the samples. Typical areas include sample enhancement, feature extraction, classification, indexing, fusion, normalization, and anti-spoofing. In this context, computational intelligence plays an important role in performing of complex non-linear computations by creating models from the training data. These approaches are based on supervised as well as unsupervised training techniques. This work presents computational intelligence techniques applied to biometrics, from both a theoretical and an application point of view.
13 citations
01 Sep 2016
TL;DR: This paper proposes a brief review of the most important CI techniques applied in each step of the design of a monitoring and control system for industrial and environmental applications, and describes how these techniques are integrated in the development of efficient industrial andEnvironmental applications.
Abstract: Computational Intelligence (CI) techniques are being increasingly used for automatic monitoring and control systems, especially regarding industrial and environmental applications, due to their performance, their capabilities in fusing noisy or incomplete data obtained from heterogeneous sensors, and the ability in adapting to variations in the operational conditions. Moreover, the increase in the computational power and the decrease of the size of the computing architectures allowed a more pervasive use of CI techniques in a great variety of situations. In this paper, we propose a brief review of the most important CI techniques applied in each step of the design of a monitoring and control system for industrial and environmental applications, and describe how these techniques are integrated in the development of efficient industrial and environmental applications.
12 citations
15 May 2016
TL;DR: Simulation results validate that the proposed control design ensures promising performance and is able to cope with unexpected surgical stimulations and anesthetic interactions during surgical procedures.
Abstract: In recent years automated anesthesia has gained much interest. In this paper the applicability of the Fixed Point Transformation-based Adaptive Control design for automatic control of the depth of hypnosis during surgical operation has been investigated. The applied control design assumes the availability of the rough model of the dynamic system under control. The here presented technique regulates the WAVCNS index as the only measurable variable by controling the intravenous propofol administration. Simulation results validate that the proposed control design ensures promising performance and is able to cope with unexpected surgical stimulations and anesthetic interactions during surgical procedures.
10 citations
01 Sep 2016
TL;DR: A framework for the biometric fusion in ABC systems is proposed, with the features of being technology-neutral and privacy- compliant, by performing an analysis of the most suitable biometrics fusion techniques for ABC systems and considering the current technical and legal limitations.
Abstract: 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.
7 citations
01 Sep 2016
TL;DR: This paper illustrates an approach for allowing users to specify their requirements and preferences over these requirements, and to consider them in the definition of a SLA.
Abstract: The use of cloud services is typically regulated by a Service Level Agreement (SLA) that defines the specific parameters of the service that will be provided by a Cloud Provider. The ability of users to negotiate a SLA that meets their requirements is a key issue in cloud computing. In this paper, we illustrate an approach for allowing users to specify their requirements and preferences over these requirements, and to consider them in the definition of a SLA.
6 citations
01 Dec 2016
TL;DR: A privacy-compliant and adaptive normalization approach for enhancing fingerprint recognition in ABC systems is proposed that computes cohort scores from an external public dataset and uses computational intelligence to learn and improve the matching score distribution.
Abstract: Automated Border Control (ABC) systems are being increasingly used to perform a fast, accurate, and reliable verification of the travelers' identity. These systems use biometric technologies to verify the identity of the person crossing the border. In this context, fingerprint verification systems are widely adopted due to their high accuracy and user acceptance. Matching score normalization methods can improve the performance of fingerprint recognition in ABC systems and mitigate the effect of non-idealities typical of this scenario without modifying the existing biometric technologies. However, privacy protection regulations restrict the use of biometric data captured in ABC systems and can compromise the applicability of these techniques. Cohort score normalization methods based only on impostor scores provide a suitable solution, due to their limited use of sensible data and to their promising performance. In this paper, we propose a privacy-compliant and adaptive normalization approach for enhancing fingerprint recognition in ABC systems. The proposed approach computes cohort scores from an external public dataset and uses computational intelligence to learn and improve the matching score distribution. The use of a public dataset permits to apply cohort normalization strategies in contexts in which privacy protection regulations restrict the storage of biometric data. We performed a technological and a scenario evaluation using a commercial matcher currently adopted in real ABC systems and we used data simulating different conditions typical of ABC systems, obtaining encouraging results.
TL;DR: In this paper, several computational intelligence paradigms (namely, Fuzzy C-Means, Radial Basis Function Networks, k-Nearest Neighbor, and Feed-forward Neural Networks) are challenged in the task of identifying the MPP power from the working condition directly measurable from the solar panel, such as the voltage, V, current, I, and the temperature, T, of the panel.
27 Jun 2016
TL;DR: This work presents a framework for quality estimation based on image processing algorithms that avoids segmentation and has been tested using synthetic and real images with different noise conditions, illustrating the robustness of the approach and its capability to detect significant quality changes in the wood particles.
Abstract: 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.
26 Aug 2016
TL;DR: In this paper, the Stretched Sigmoid Functions have been investigated in order to obtain a more precise positioning of the function in the vicinity of the solution of the control task.
Abstract: In adaptive nonlinear control Lyapunov's 2nd or Direct method became a fundamental tool in control design. Recently the application of the “Sigmoid Generated Fixed Point Transformation (SGFPT)” has been introduced for replacing the Lyapunov technique. This systematic method has been presented for the generation of whole families of Fixed Point Transformations and has been extended from Single Input Single Output (SISO) to Multiple Input Multiple Output (MIMO) systems. Furthermore, the Stretched Sigmoid Functions have been introduced. In this paper a new function of this family have been investigated in order to obtain a more precise positioning of the function in the vicinity of the solution of the control task. The applicability and effectiveness of the proposed control method have been confirmed by the adaptive control of the inverted pendulum with vertical vibration of the pivot, i.e. the so-called Kapitza's pendulum. Results of numerical simulations have revealed that the proposed control design ensures performance enhancement.
23 Dec 2016
TL;DR: This paper first uses a unsupervised algorithm, K-means clustering, which can learn the features without known the class of training samples, for radar target recognition, and proposes a method of data augmentation to get more data for the un supervised algorithm.
Abstract: Synthetic aperture radar (SAR) automatic target recognition (ATR) has been receiving more and more attention in the past two decades. A lot of methods have been proposed and studied for radar target recognition. Among some of these methods, they use the supervised algorithms to extracts features. In this paper, we first use a unsupervised algorithm, K-means clustering, which can learn the features without known the class of training samples, for radar target recognition. As the unsupervised algorithm has a high demand on the scale of the data, so we proposed a method of data augmentation to get more data for the unsupervised algorithm, by which the K-means clustering algorithm can learn more unsupervised features. Experimental results on the MSTAR database show that the proposed method can achieve satisfying recognition accuracy compared with other state-of-the-art methods.
07 Nov 2016
TL;DR: Fuzzy logic is widely used for modeling complex and ill-defined systems and is applied in the SGFPT control design to validate that the presented technique fulfills the performance criteria.
Abstract: The great majority of the adaptive nonlinear control design are based on Lyapunov's 2nd or commonly referred to as the Direct method. In the last years the “Sigmoid Generated Fixed Point Transformation (SGFPT)” has been introduced for replacing the Lyapunov technique. This systematic method has been proposed for the generation of whole families of Fixed Point Transformations. In addition it has been extended from Single Input Single Output (SISO) to Multiple Input Multiple Output (MIMO) systems. Recently, several model building issues are increasingly replaced by soft-computing based methods. In spite of the classical hard-computing methods the intelligent methodologies are able to deal with imprecisions, uncertainties, etc. by an efficient and robust way. Fuzzy logic is widely used for modeling complex and ill-defined systems. In this paper we apply the fuzzy modeling in the SGFPT control design. The applicability of the proposed scheme is confirmed by the adaptive control of the inverted pendulum system. In the investigations an “affine”, and a “soft computing-based” model were compared. Simulation results validate that the presented technique fulfills the performance criteria.
01 Jan 2016
TL;DR: Simultaneous Perturbation Stochastic Approximation (SPSA) Optimization Adrienn Dineva*, Annamaria R. Varkonyi-Koczy**, Jozsef K. Tar*** and Vincenzo Piuri****.
Abstract: Simultaneous Perturbation Stochastic Approximation (SPSA) Optimization Adrienn Dineva*, Annamaria R. Varkonyi-Koczy**, Jozsef K. Tar*** and Vincenzo Piuri**** *Doctoral School of Applied Informatics and Applied Mathematics, Obuda University, Budapest, Hungary *Doctoral School of Computer Science, Universita’ degli Studi di Milano, Crema, Italy **Institute of Mechatronics & Vehicle Engineering, Obuda University, Budapest Hungary ** Department of Mathematics and Informatics, J. Selye University, Komarno, Slovakia ***Institute of Applied Mathematics, Obuda University, Budapest, Hungary **** Department of Computer Science, Universita’ degli Studi di Milano, Crema, Italy E-mail: * dineva.adrienn@bgk.uni-obuda.hu, ** varkonyi-koczy@uni-obuda.hu, ***tar.jozsef@nik.uni-obuda.hu, ****vincenzo.piuri@unimi.it
24 Aug 2016
TL;DR: A new preprocessing strategy with the combination of fuzzy information measure and wavelets that allows precise feature extraction, denoising and additionally effective compression of the point cloud is introduced.
Abstract: Processing of remotely sensed point clouds is a crucial issue for many applications, including robots operating autonomously in real world environments, etc. The pre-processing is almost an important step which includes operations such as remove of systematic errors, filtering, feature detection and extraction. In this paper we introduce a new preprocessing strategy with the combination of fuzzy information measure and wavelets that allows precise feature extraction, denoising and additionally effective compression of the point cloud. The suitable setting of the applied wavelet and parameters have a great impact on the result and depends on the aim of preprocessing that usually is a challenge for the users. In order to address this problem the fuzzy information measure has been proposed that supports the adaptive setting of the parameters. Additionally a fuzzy performance measure has been introduced, that can reduce the complexity of the procedure. Simulation results validate the efficiency and applicability of this method.