The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Mengchu Zhou - One of the best experts on this subject based on the ideXlab platform.
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a petri net based discrete event control of automated manufacturing systems with assembly operations
IEEE Transactions on Control Systems and Technology, 2015Co-Authors: Hesuan Hu, Mengchu ZhouAbstract:In the context of automated manufacturing systems (AMSs), Petri nets are widely adopted to solve the modeling, analysis, and control problems. So far, nearly all known approaches to Liveness-enforcing supervisory control study AMSs with flexible routes, whereas little work investigates the ones with synchronization operations. Compared with flexibility, synchronization allows the disassembly and assembly operations that correspond to splitting to and merging from different subprocesses, respectively. Such structures bring difficulties to establish a Liveness condition of the Petri net model of AMSs. In this paper, we propose a novel class of systems, which can well deal with these features so as to facilitate the investigation of such complex systems. Using structural analysis, we show that their Liveness can be attributed to deadlock freeness, which is much easier to analyze, detect, and control by synthesizing a proper supervisory controller. Furthermore, a set of mathematical formulations is proposed to describe and extract the corresponding deadlocks. This facilitates the synthesis of Liveness enforcing supervisors as it avoids the consideration of deadlock-free but nonlive scenarios. The effectiveness and efficiency of this new method is shown by AMS examples.
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deadlock free control of automated manufacturing systems with flexible routes and assembly operations using petri nets
IEEE Transactions on Industrial Informatics, 2013Co-Authors: Hesuan Hu, Zhiwu Li, Mengchu Zhou, Ying TangAbstract:In the context of automated manufacturing systems (AMS), Petri nets are widely adopted to solve the modeling, analysis, and control problems. So far, nearly all known approaches to Liveness enforcing supervisory control investigate AMS with either flexible routes or assembly operations, whereas little work investigates them with both. In this paper, we propose a novel class of systems, which can well deal with both features so as to facilitate the control of more complex AMS. Using structural analysis, we show that Liveness of their Petri net model can be attributed to the absence of undermarked siphons, which is realizable by synthesizing a proper supervisory controller. Moreover, an efficient method is developed and verified via AMS examples.
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Hybrid Liveness-Enforcing Policy for Generalized Petri Net Models of Flexible Manufacturing Systems
IEEE transactions on systems man and cybernetics, 2013Co-Authors: Ding Liu, Mengchu ZhouAbstract:This paper proposes a hybrid Liveness-enforcing method for a class of Petri nets, which can well model many flexible manufacturing systems. The proposed method combines elementary siphons with a characteristic structure-based method to prevent deadlocks and enforce Liveness to the net class under consideration. The characteristic structure-based method is further advanced in this work. It unveils and takes a full advantage of an intrinsically live structure of generalized Petri nets, which hides behind the arc weights, to achieve the Liveness enforcement without any external control agent such as monitors. This hybrid method can identify and remove redundant monitors from a Liveness-enforcing supervisor designed according to existing policies, improve the permissiveness, reduce the structural complexity of a controlled system, and consequently save the control implementation cost. Several examples are used to illustrate this method.
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two stage method for synthesizing Liveness enforcing supervisors for flexible manufacturing systems using petri nets
IEEE Transactions on Industrial Informatics, 2006Co-Authors: Mengchu ZhouAbstract:This paper develops a two-stage approach to synthesizing Liveness-enforcing supervisors for flexible manufacturing systems (FMS) that can be modeled by a class of Petri nets. First, we find siphons that need to be controlled using a mixed integer programming (MIP) method. This way avoids complete siphon enumeration that is more time-consuming for a sizable plant model than the MIP method. Monitors are added for only those siphons that require them. Second, we rearrange the output arcs of the monitors on condition that Liveness is still preserved. The Liveness is verified by an MIP-based deadlock detection method instead of much time-consuming reachability analysis. Experimental studies show that the proposed approach is more efficient than the existing ones and can result in more permissive and structurally simpler Liveness-enforcing supervisors than all the known existing methods. This paper makes the application of siphon-based deadlock control methods to industrial-size FMS possible
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a modified reachability tree approach to analysis of unbounded petri nets
Systems Man and Cybernetics, 2004Co-Authors: Feiyue Wang, Yanqing Gao, Mengchu ZhouAbstract:Reachability trees, especially the corresponding Karp-Miller's finite reachability trees generated for Petri nets are fundamental for systematically investigating many characteristics such as boundedness, Liveness, and performance of systems modeled by Petri nets. However, too much information is lost in a FRT to render it useful for many applications. In this paper, modified reachability trees (MRT) of Petri nets are introduced that extend the capability of Karp-Miller's FRTs in solving the Liveness, deadlock, and reachability problems, and in defining or determining possible firing sequences. The finiteness of MRT is proved and several examples are presented to illustrate the advantages of MRT over FRT.
Stephanie Schuckers - One of the best experts on this subject based on the ideXlab platform.
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review of the fingerprint Liveness detection livdet competition series 2009 to 2015
Image and Vision Computing, 2017Co-Authors: Luca Ghiani, David Yambay, Gian Luca Marcialis, Fabio Roli, Valerio Mura, Stephanie SchuckersAbstract:Abstract A spoof attack, a subset of presentation attacks, is the use of an artificial replica of a biometric in an attempt to circumvent a biometric sensor. Liveness detection, or presentation attack detection, distinguishes between live and fake biometric traits and is based on the principle that additional information can be garnered above and beyond the data procured by a standard authentication system to determine if a biometric measure is authentic. The goals for the Liveness Detection (LivDet) competitions are to compare software-based fingerprint Liveness detection and artifact detection algorithms (Part 1), as well as fingerprint systems which incorporate Liveness detection or artifact detection capabilities (Part 2), using a standardized testing protocol and large quantities of spoof and live tests. The competitions are open to all academic and industrial institutions which have a solution for either software-based or system-based fingerprint Liveness detection. The LivDet competitions have been hosted in 2009, 2011, 2013 and 2015 and have shown themselves to provide a crucial look at the current state of the art in Liveness detection schemes. There has been a noticeable increase in the number of participants in LivDet competitions as well as a noticeable decrease in error rates across competitions. Participants have grown from four to the most recent thirteen submissions for Fingerprint Part 1. Fingerprints Part 2 has held steady at two submissions each competition in 2011 and 2013 and only one for the 2015 edition. The continuous increase of competitors demonstrates a growing interest in the topic.
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livdet 2015 fingerprint Liveness detection competition 2015
International Conference on Biometrics: Theory Applications and Systems, 2015Co-Authors: Valerio Mura, David Yambay, Luca Ghiani, Gian Luca Marcialis, Fabio Roli, Stephanie SchuckersAbstract: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 authentication system, and this additional data can be used to determine if a biometric measure is authentic.
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livdet iris 2013 iris Liveness detection competition 2013
International Journal of Central Banking, 2014Co-Authors: David Yambay, James S Doyle, Kevin W Bowyer, Adam Czajka, Stephanie SchuckersAbstract:The use of an artificial replica of a biometric characteristic in an attempt to circumvent a system is an example of a biometric presentation attack. Liveness detection is one of the proposed countermeasures, and has been widely implemented in fingerprint and iris recognition systems in recent years to reduce the consequences of spoof attacks. The goal for the Liveness Detection (LivDet) competitions is to compare software-based iris Liveness detection methodologies using a standardized testing protocol and large quantities of spoof and live images. Three submissions were received for the competition Part 1; Biometric Recognition Group de Universidad Autonoma de Madrid, University of Naples Federico II, and Faculdade de Engenharia de Universidade do Porto. The best results from across all three datasets was from Federico with a rate of falsely rejected live samples of 28.6% and the rate of falsely accepted fake samples of 5.7%.
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livdet 2011 fingerprint Liveness detection competition 2011
International Conference on Biometrics, 2012Co-Authors: David Yambay, Luca Ghiani, Paolo Denti, Gian Luca Marcialis, Fabio Roli, Stephanie SchuckersAbstract:“Liveness detection”, a technique used to determine the vitality of a submitted biometric, has been implemented in fingerprint scanners in recent years. The goal for the LivDet 2011 competition is to compare software-based fingerprint Liveness detection methodologies (Part 1), as well as fingerprint systems which incorporate Liveness detection capabilities (Part 2), using a standardized testing protocol and large quantities of spoof and live fingerprint images. This competition was open to all academic and industrial institutions which have a solution for either software-based or system-based fingerprint vitality detection problem. Five submissions across the two parts of the competition resulted in successful completion. These submissions were: Chinese Academy of Sciences Institute of Automation (CASIA), Federico II University (Federico) and Dermalog Identification SystemsGmbH (Dermalog) for Part 1: Algorithms, and GreenBit and Dermalog for Part 2: Systems. Part 1 was evaluated using four different datasets. The best results were from Federico on the Digital Persona dataset with error for live and spoof detection of 6.2% and 11.61% respectively. The best overall results for Part 1 were Dermalog with 34.05 FerrFake and 11.825% FerrLive. Part 2 was evaluated using live subjects and spoof finger casts. The best results were from Dermalog with an error for live and spoof of 42.5% and 0.8%, respectively.
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integrating a wavelet based perspiration Liveness check with fingerprint recognition
Pattern Recognition, 2009Co-Authors: Aditya Abhyankar, Stephanie SchuckersAbstract:It has been shown that fingerprint scanners can be deceived very easily, using simple, inexpensive techniques. In this work, a countermeasure against such attacks is enhanced, that utilizes a wavelet based approach to detect Liveness, integrated with the fingerprint matcher. Liveness is determined from perspiration changes along the fingerprint ridges, observed only in live people. The proposed algorithm was applied to a data set of approximately 58 live, 50 spoof and 28 cadaver fingerprint images captured at 0 and 2s, from each of three different types of scanners, for normal conditions. The results demonstrate perfect separation of live and not live for the normal conditions. Without Liveness module the commercially available verifinger matcher is shown to give equal error rate (EER) of 13.85% where false reject rate is calculated for genuine-live users and false accept rate is for genuine-not live, imposter-live and imposter-not live. The integrated system of fingerprint matcher and Liveness module reduces EER to 0.03%. Results are also presented for moist and dry fingers simulated by glycerin and acetone, respectively. The system is further tested using gummy fingers and various deliberately simulated conditions including pressure change and adding moisture to the spoof to analyze the strength of the Liveness algorithm.
Rubens C Machado - One of the best experts on this subject based on the ideXlab platform.
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fingerprint Liveness detection using convolutional neural networks
IEEE Transactions on Information Forensics and Security, 2016Co-Authors: Rodrigo Nogueira, Roberto De Alencar Lotufo, Rubens C MachadoAbstract:With the growing use of biometric authentication systems in the recent years, spoof fingerprint detection has become increasingly important. In this paper, we use convolutional neural networks (CNNs) for fingerprint Liveness detection. Our system is evaluated on the data sets used in the Liveness detection competition of the years 2009, 2011, and 2013, which comprises almost 50 000 real and fake fingerprints images. We compare four different models: two CNNs pretrained on natural images and fine-tuned with the fingerprint images, CNN with random weights, and a classical local binary pattern approach. We show that pretrained CNNs can yield the state-of-the-art results with no need for architecture or hyperparameter selection. Data set augmentation is used to increase the classifiers performance, not only for deep architectures but also for shallow ones. We also report good accuracy on very small training sets (400 samples) using these large pretrained networks. Our best model achieves an overall rate of 97.1% of correctly classified samples—a relative improvement of 16% in test error when compared with the best previously published results. This model won the first prize in the fingerprint Liveness detection competition 2015 with an overall accuracy of 95.5%.
Fabio Roli - One of the best experts on this subject based on the ideXlab platform.
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review of the fingerprint Liveness detection livdet competition series 2009 to 2015
Image and Vision Computing, 2017Co-Authors: Luca Ghiani, David Yambay, Gian Luca Marcialis, Fabio Roli, Valerio Mura, Stephanie SchuckersAbstract:Abstract A spoof attack, a subset of presentation attacks, is the use of an artificial replica of a biometric in an attempt to circumvent a biometric sensor. Liveness detection, or presentation attack detection, distinguishes between live and fake biometric traits and is based on the principle that additional information can be garnered above and beyond the data procured by a standard authentication system to determine if a biometric measure is authentic. The goals for the Liveness Detection (LivDet) competitions are to compare software-based fingerprint Liveness detection and artifact detection algorithms (Part 1), as well as fingerprint systems which incorporate Liveness detection or artifact detection capabilities (Part 2), using a standardized testing protocol and large quantities of spoof and live tests. The competitions are open to all academic and industrial institutions which have a solution for either software-based or system-based fingerprint Liveness detection. The LivDet competitions have been hosted in 2009, 2011, 2013 and 2015 and have shown themselves to provide a crucial look at the current state of the art in Liveness detection schemes. There has been a noticeable increase in the number of participants in LivDet competitions as well as a noticeable decrease in error rates across competitions. Participants have grown from four to the most recent thirteen submissions for Fingerprint Part 1. Fingerprints Part 2 has held steady at two submissions each competition in 2011 and 2013 and only one for the 2015 edition. The continuous increase of competitors demonstrates a growing interest in the topic.
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livdet 2015 fingerprint Liveness detection competition 2015
International Conference on Biometrics: Theory Applications and Systems, 2015Co-Authors: Valerio Mura, David Yambay, Luca Ghiani, Gian Luca Marcialis, Fabio Roli, Stephanie SchuckersAbstract: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 authentication system, and this additional data can be used to determine if a biometric measure is authentic.
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fingerprint Liveness detection using binarized statistical image features
International Conference on Biometrics: Theory Applications and Systems, 2013Co-Authors: Luca Ghiani, Gian Luca Marcialis, Abdenour Hadid, Fabio RoliAbstract:Recent experiments, reported in the third edition of Fingerprint Liveness Detection competition (LivDet 2013), have clearly shown that fingerprint Liveness detection is a very difficult and challenging task. Although the number of approaches is large, none of them can be claimed as able to detect Liveness of fingerprint traits with an acceptable error rate. In our opinion, in order to investigate at which extent this error can be reduced, novel feature sets must be proposed, and, eventually, integrated with existing ones. In this paper, a novel fingerprint Liveness descriptor named “BSIF” is described, which, similarly to Local Binary Pattern and Local Phase Quantization-based representations, encodes the local fingerprint texture on a feature vector. Experimental results on LivDet 2011 data sets appear to be encouraging and make this descriptor worth of further investigations.
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fingerprint Liveness detection using binarized statistical image features
International Conference on Biometrics: Theory Applications and Systems, 2013Co-Authors: Luca Ghiani, Gian Luca Marcialis, Abdenour Hadid, Fabio RoliAbstract:Recent experiments, reported in the third edition of Fingerprint Liveness Detection competition (LivDet 2013), have clearly shown that fingerprint Liveness detection is a very difficult and challenging task. Although the number of approaches is large, none of them can be claimed as able to detect Liveness of fingerprint traits with an acceptable error rate. In our opinion, in order to investigate at which extent this error can be reduced, novel feature sets must be proposed, and, eventually, integrated with existing ones. In this paper, a novel fingerprint Liveness descriptor named “BSIF” is described, which, similarly to Local Binary Pattern and Local Phase Quantization-based representations, encodes the local fingerprint texture on a feature vector. Experimental results on LivDet 2011 data sets appear to be encouraging and make this descriptor worth of further investigations.
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livdet 2013 fingerprint Liveness detection competition 2013
International Conference on Biometrics, 2013Co-Authors: Luca Ghiani, David Yambay, Gian Luca Marcialis, Fabio Roli, Valerio Mura, Simona Tocco, Stephanie SchuckcrsAbstract: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.
Carlo Sansone - One of the best experts on this subject based on the ideXlab platform.
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local contrast phase descriptor for fingerprint Liveness detection
Pattern Recognition, 2015Co-Authors: Diego Gragnaniello, Giovanni Poggi, Carlo Sansone, Luisa VerdolivaAbstract:We propose a new local descriptor for fingerprint Liveness detection. The input image is analyzed both in the spatial and in the frequency domain, in order to extract information on the local amplitude contrast, and on the local behavior of the image, synthesized by considering the phase of some selected transform coefficients. These two pieces of information are used to generate a bi-dimensional contrast-phase histogram, used as feature vector associated with the image. After an appropriate feature selection, a trained linear-kernel SVM classifier makes the final live/fake decision. Experiments on the publicly available LivDet 2011 database, comprising datasets collected from various sensors, prove the proposed method to outperform the state-of-the-art Liveness detection techniques. HighlightsWe propose a new local descriptor for fingerprint Liveness detection.It is based on the joint use of contrast and phase information.Image analysis is carried out in both the spatial and the transform domains.We generate a bi-dimensional contrast-phase histogram, used as feature vector.A properly trained linear-kernel SVM classifier makes the final live/fake decision.
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fingerprint Liveness detection based on weber local image descriptor
2013 IEEE Workshop on Biometric Measurements and Systems for Security and Medical Applications, 2013Co-Authors: Diego Gragnaniello, Giovanni Poggi, Carlo Sansone, Luisa VerdolivaAbstract:In this paper, we investigate the use of a local discriminative feature space for fingerprint Liveness detection. In particular, we rely on the Weber Local Descriptor (WLD), which is a powerful and robust descriptor recently proposed for texture classification. Inspired by Weber's law, it consists of two components, differential excitation and orientation, evaluated for each pixel of the image. Joint histograms of these components are then processed to build the discriminative features used to train a linear kernel SVM classifier. Experimental results with different databases and different sensors show WLD to perform favorably compared to the state-of-the-art methods in fingerprint Liveness detection. In addition, by combining WLD with LPQ (Local Phase Quantization) results further improve significantly.
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combining perspiration and morphology based static features for fingerprint Liveness detection
Pattern Recognition Letters, 2012Co-Authors: Emanuela Marasco, Carlo SansoneAbstract:It has been showed that, by employing fake fingers, the existing fingerprint recognition systems may be easily deceived. So, there is an urgent need for improving their security. Software-based Liveness detection algorithms typically exploit morphological and perspiration-based characteristics separately to measure the vitality. Both such features provide discriminant information about live and fake fingers, then, it is reasonable to investigate also their joint contribution. In this paper, we combine a set of the most robust morphological and perspiration-based measures. The effectiveness of the proposed approach has been assessed through a comparison with several state-of-the-art techniques for Liveness detection. Experiments have been carried out, for the first time, by adopting standard databases. They have been taken from the Liveness Detection Competition 2009 whose data have been acquired by using three different optical sensors. Further, we have analyzed how the performance of our algorithm changes when the material employed for the spoof attack is not available during the training of the system.
