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第37届中国控制会议

作者： Wojciech Paszke Eric Rogers Hongfeng Tao Krzysztof Ga?kowski Institute of Control and Computation Engineering University of Zielona Góra Department of Electronics and Computer Science University of Southampton Key Laboratory of Advanced Process Control for Light Industry of Ministry of Education Jiangnan University

This paper deals with the problem of designing an iterative learning control scheme for uncertain time-delay systems using differential repetitive process stability theory. The resulting design produces a stabilizing feedback controller in the time domain and a feedforward controller that guarantees monotonic convergence in the trial-to-trial domain for all admissible uncertainties. The design procedures are delay dependent since the obtained results depend on the size of state delay and are also extended to limited frequency range design specification. A new design procedure is developed in terms of linear matrix inequalities, which guarantee prescribed performance specifications. Finally, a simulation example is given to illustrate the application of the new results.

关键词： Iterative learning control time-delay systems polytopic uncertainty finite frequency domain

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A quadratic boundedness approach to fault tolerant control for nonlinear system

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IFAC-PapersOnLine 2017年第1期50卷 2101-2106页

作者： Buciakowski M. Witczak M. Korbicz J. Institute of Control and Computation Engineering University of Zielona Góra ul. Podgórna 50 Zielona Góra 65–246 Poland

In the paper, a methodology of actuator fault estimation and fault tolerant control for nonlinear systems is proposed. To solve such a challenging problem, a quadratic boundedness (QB) approach is used to design observer in such a way that the state and fault estimation error will converge to the origin (as close as possible) irrespective of the external disturbances. The same approach (QB) is used to design fault tolerant control to compensate the effect of an actuator fault in system, which is suitably integrated with state and fault estimation schemes. Sufficient conditions for the existence and stability of the proposed state and fault estimation scheme as well as the fault tolerant control are expressed in the form of linear matrix inequalities (LMIs). To show effectiveness of the proposed approach, the final part of the paper presents simulation results concerning a quadruple–tank system. © 2017

关键词： diagnosis fault–tolerant filter design nonlinear observers

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Cross-spectral iris recognition for mobile applications using high-quality color images

arXiv

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arXiv 2018年

作者： Trokielewicz, Mateusz Bartuzi, Ewelina Kolska st 12 Warsaw01-045 Poland Institute of Control and Computation Engineering Warsaw University of Technology Nowowiejska st 15/19 Warsaw00-665 Poland

With the recent shift towards mobile computing, new challenges for biometric authentication appear on the horizon. This paper provides a comprehensive study of cross-spectral iris recognition in a scenario, in which high quality color images obtained with a mobile phone are used against enrollment images collected in typical, near-infrared setups. Grayscale conversion of the color images that employs selective RGB channel choice depending on the iris coloration is shown to improve the recognition accuracy for some combinations of eye colors and matching software, when compared to using the red channel only, with equal error rates driven down to as low as 2%. The authors are not aware of any other paper focusing on cross-spectral iris recognition is a scenario with near-infrared enrollment using a professional iris recognition setup and then a mobile-based verification employing color images. Copyright © 2018, The Authors. All rights reserved.

关键词： Biometrics

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Linear regression analysis of template aging in iris biometrics

arXiv

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arXiv 2018年

作者： Trokielewicz, Mateusz Biometrics Laboratory Research and Academic Computer Network NASK Wawozowa 18 Warsaw Poland Institute of Control and Computation Engineering Warsaw University of Technology Nowowiejska 15/19 Warsaw Poland

The aim of this work is to determine how vulnerable different iris coding methods are in relation to biometric template aging phenomenon. This is considered to be particularly important when the time lapse between gallery and probe samples extends significantly, to more than a few years. Our experiments employ iris aging analysis conducted using three different iris recognition algorithms and a database of 583 samples from 58 irises collected up to nine years apart. To determine the degradation rates of similarity scores with extending time lapse and also in relation to multiple image quality and geometrical factors of sample images, a linear regression analysis was performed. 29 regression models have been tested with both the time parameter and geometrical factors being statistically significant in every model. Quality measures that showed statistically significant influence on the predicted variable were, depending on the method, image sharpness and local contrast or their mutual relations. To our best knowledge, this is the first paper describing aging analysis using multiple regression models with data covering such a wide time period. Results presented suggest that template aging effect occurs in iris biometrics to a statistically significant extent. Image quality and geometrical factors may contribute to the degradation of similarity score. However, the estimate of time parameter showed statistical significance and similar value in each of the tested models. This reveals that the aging phenomenon may as well be unrelated to quality and geometrical measures of the image. Copyright © 2018, The Authors. All rights reserved.

关键词： Biometrics

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Dissipative Stabilization of Nonlinear Repetitive Processes with an Iterative Learning control Application

Dissipative Stabilization of Nonlinear Repetitive Processes ...

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American control Conference

作者： Julia Emelianova Pavel Pakshin Krzvsztof Galkowski Eric Rogers Arzamas Polytechnic Institute of R.E. Alek-seev Nizhny Novgorod State Technical University 19 Kalinina Street Arzamas 607227 Russia and Lobachevsky State University of Nizhny Novgorod Prospekt Gagarina 23 Nizhny Novgorod 603950 Russia Institute of Control and Computation Engineering University of Zielona Gora Podgorna 50 Zielona Gora 65-246 Poland School of Electronics and Computer Science University of Southampton Southampton SO17 1BJ UK

ISBN: (纸本)9781538679012;9781538679265

Repetitive processes arise in the modeling of physical systems and have a 2D systems structure as there are two directions of information propagation, one of which is spatial and the other is time over a finite duration. Recently, a stability theory for nonlinear repetitive processes has been developed. This paper uses nonlinear stability theory to develop a parameterized control law for linear dynamics. Applied to iterative learning control law design, this law allows for gain scheduling tuning to achieve better performance. The new design is illustrated by application to an example where the model representing the dynamics has been obtained by frequency response test data obtained from a physical example.

关键词： control law stability theory Repetitive nonlinearity dynamics Modeling

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Thermal Features for Presentation Attack Detection in Hand Biometrics

Thermal Features for Presentation Attack Detection in Hand B...

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IEEE International Conference on Biometrics: Theory, Applications, and Systems (BTAS)

作者： Ewelina Bartuzi Mateusz Trokielewicz Biometrics and Machine Intelligence Lab Research and Academic Computer Network Kolska 12 Warsaw Poland Institute of Control and Computation Engineering Warsaw University of Technology Nowowiejska 15/19 Warsaw Poland

ISBN: (纸本)9781538671818;9781538671801

This paper proposes a method for utilizing thermal features of the hand for the purpose of presentation attack detection (PAD) that can be employed in a hand biometrics system's pipeline. By envisaging two different operational modes of our system, and by employing a DCNN-based classifiers fine-tuned with a dataset of real and fake hand representations captured in both visible and thermal spectrum, we were able to bring two important deliverables. First, a PAD method operating in an open-set mode, capable of correctly discerning 100% of fake thermal samples, achieving Attack Presentation Classification Error Rate (APCER) and Bona-Fide Presentation Classification Error Rate (BPCER) equal to 0%, which can be easily implemented into any existing system as a separate component. Second, a hand biometrics system operating in a closed-set mode, that has PAD built right into the recognition pipeline, and operating simultaneously with the user-wise classification, achieving rank-1 recognition accuracy of up to 99.75%. We also show that thermal images of the human hand, in addition to liveness features they carry, can also improve classification accuracy of a biometric system, when coupled with visible light images. To follow the reproducibility guidelines and to stimulate further research in this area, we share the trained model weights, source codes, and a newly created dataset of fake hand representations with interested researchers.

关键词： Biometrics (access control) Databases Training Pipelines Feature extraction Error analysis Task analysis

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Unconstrained biometric recognition based on thermal hand images

Unconstrained biometric recognition based on thermal hand im...

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International Workshop on Biometrics and Forensics (IWBF)

作者： Ewelina Bartuzi Katarzyna Roszczewska Adam Czajka Andrzej Pacut Biometrics Laboratory Research and Academic Computer Network (NASK) Warsaw Poland Department of Computer Science and Engineering University of Notre Dame Notre Dame IN USA Institute of Control and Computation Engineering Warsaw University of Technology Warsaw Poland

ISBN: (纸本)9781538613672

This paper proposes a biometric recognition method based on thermal images of inner part of the hand, and a database of 21,000 thermal images of both hands acquired by a specialized thermal camera from 70 subjects. The data for each subject was acquired in three different sessions, with two first sessions organized on the same day, and the third session organized approximately two weeks apart. This allowed to analyze the stability of hand temperature in both short-term and long-term horizons. No hand stabilization or positioning devices were used during acquisition, making this setup closer to real-world, unconstrained applications. This required making our method translation-, rotation- and scale-invariant. Two approaches for feature selection and classification are proposed and compared: feature engineering deploying texture descriptors such as Binarized Statistical Image Features (BSIF) and Gabor wavelets, and feature learning based on convolutional neural networks (CNN) trained in different environmental conditions. For within-session scenario we achieved 0.36% and 0.00% of equal error rate (EER) in the first and the second approach, respectively. Between-session EER stands at 27.98% for the first approach and 17.17% for the second one. These results allow for estimation of a short-term stability of hand thermal information. This paper presents the first known to us database of hand thermal images and the first biometric system based solely on hand thermal maps acquired by thermal sensor in unconstrained scenario.

关键词： Feature extraction Thermal sensors Image segmentation Veins Databases Temperature measurement Biomedical imaging

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Learning music time series with a parallel hybrid forecasting model calibrated with Reduced VNS

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Electronic Notes in Discrete Mathematics 2018年 66卷 215-222页

作者： Coelho, Mateus N. Coelho, Vitor N. Coelho, Igor M. Coelho, Bruno N. Souza, Marcone J.F. Control and Automation Engineering Universidade Federal de Ouro Preto Ouro Preto Brazil Grupo da Causa Humana Ouro Preto Brazil Institute of Computation Universidade Federal Fluminense Niterói RJ Brazil Department of Computer Science Universidade Federal de Ouro Preto Ouro Preto MG Brazil Department of Computer Science Universidade do Estado do Rio de Janeiro Rio de Janeiro Brazil

This paper focus on the learning of music time series. In this context, from compressed digital audio files, we sought to verify how a song can be learned, both in terms of amplitude and frequency. Given the enormous amount of data contained in those time series, the use of classical learning methods becomes limited. Typical compressed acquisitions in MP3 files usually contains 44100 samples per second. In this context, the use of metaheuristic algorithms for this learning task, in a big-data environment, is justified, and the use of deep learning techniques sounds necessary. In this paper, a Hybrid Forecasting Model, calibrated with the Reduced Variable Neighborhood Search, with parallel processing using Graphical Processing Units, is used as a deep learning tool. Case studies composed of simple musical compositions are used for verifying the potential of the method for such application. We suggest that the techniques investigated here can also be used for the learning, classification and computational music composition. © 2018 Elsevier B.V.

关键词： Deep Learning Digital audio Hybrid Forecasting Model Music Time Series Reduced Variable Neighborhood Search

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Presentation Attack Detection for Cadaver Iris

Presentation Attack Detection for Cadaver Iris

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IEEE International Conference on Biometrics: Theory, Applications, and Systems (BTAS)

作者： Mateusz Trokielewicz Adam Czajka Piotr Maciejewicz Institute of Control and Computation Engineering Warsaw University of Technology Nowowiejska 15/19 Warsaw Poland Department of Computer Science and Engineering University of Notre Dame IN USA Department of Ophthalmology Medical University of Warsaw Lindleya 4 Warsaw Poland

ISBN: (纸本)9781538671818;9781538671801

This paper presents a deep-learning-based method for iris presentation attack detection (PAD) when iris images are obtained from deceased people. Post-mortem iris recognition, despite being a potentially useful method that could aid forensic identification, can also pose challenges when used inappropriately, i.e. utilizing a dead organ of a person in an unauthorized way. Our approach is based on the VGG-16 architecture fine-tuned with a database of 574 post-mortem, near-infrared iris images from the WarsawBioBase-PostMortem-Iris-v1 database, complemented by a dataset of 256 images of live irises, collected within the scope of this study. Experiments described in this paper show that our approach is able to correctly classify iris images as either representing a live or a dead eye in almost 99% of the trials, averaged over 20 subject-disjoint, train/test splits. We also show that the post-mortem iris detection accuracy increases as time since death elapses, and that we are able to construct a classification system with APCER=0%@BPCER≈1% (Attack Presentation and Bona Fide Presentation Classification Error Rates, respectively) when only post-mortem samples collected at least 16 hours post-mortem are considered. Since acquisitions of ante- and post-mortem samples differ significantly, we applied countermeasures to minimize bias in our classification methodology caused by image properties that are not related to the PAD. This included using the same iris sensor in collection of ante- and post-mortem samples, and analysis of class activation maps to ensure that discriminant iris regions utilized by our classifier are related to properties of the eye, and not to those of the acquisition protocol. This paper offers the first known to us PAD method in a postmortem setting, together with an explanation of the decisions made by the convolutional neural network. Along with the paper we offer source codes, weights of the trained network, and a dataset of live iris images to faci

关键词： Iris recognition Iris Training Cadaver Data models Protocols Convolutional neural networks

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Modreg: A Modular Framework for RGB-D Image Acquisition and 3D Object Model Registration

Modreg: A Modular Framework for RGB-D Image Acquisition and ...

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作者： Kornuta, Tomasz Stefańczyk, MacIej IBM Research Almaden 650 Harry Rd San JoseCA United States Warsaw University of Technology Institute of Control and Computation Engineering Poland

RGB-D sensors became a standard in robotic applications requiring object recognition, such as object grasping and manipulation. A typical object recognition system relies on matching of features extracted from RGB-D images retrieved from the robot sensors with the features of the object models. In this paper we present ModReg: A system for registration of 3D models of objects. The system consists of a modular software associated with a multi-camera setup supplemented with an additional pattern projector, used for the registration of high-resolution RGB-D images. The objects are placed on a fiducial board with two dot patterns enabling extraction of masks of the placed objects and estimation of their initial poses. The acquired dense point clouds constituting subsequent object views undergo pairwise registration and at the end are optimized with a graph-based technique derived from SLAM. The combination of all those elements resulted in a system able to generate consistent 3D models of objects. © 2017 Tomasz Kornuta et al.

关键词： Object recognition

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