Mass Evidence Accumulation and Traveler Risk Scoring Engine in e-Border Infrastructure

This paper is concerned with mass evidence accumulation and risk assessment in a particular component of transportation systems and e-borders. We outline the challenges faced by contemporary border control technology and conduct a series of demonstrative experiments that cover critical scenarios, tasks, and states of both evidence accumulation and the traveler risk scoring engine. Using technology gap navigator methodology, this paper suggests an approach to traveler risk estimation based on a unified inference platform, such as a causal graphical model with various incorporated metrics of uncertainty.

[1]  G. Klir,et al.  Uncertainty-based information: Elements of generalized information theory (studies in fuzziness and soft computing). , 1998 .

[2]  L. Fiondella,et al.  Security and performance analysis of a passenger screening checkpoint for mass-transit systems , 2012, 2012 IEEE Conference on Technologies for Homeland Security (HST).

[3]  Philippe Smets,et al.  Target identification based on the transferable belief model interpretation of dempster-shafer model , 2004, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[4]  Richa Singh,et al.  Effect of illicit drug abuse on face recognition , 2016, 2016 IEEE Winter Conference on Applications of Computer Vision (WACV).

[5]  Olivier Touret,et al.  Managing the Border, Smartly , 2013, 2013 European Intelligence and Security Informatics Conference.

[6]  Arun Ross,et al.  What Else Does Your Biometric Data Reveal? A Survey on Soft Biometrics , 2016, IEEE Transactions on Information Forensics and Security.

[7]  Andrew Zisserman,et al.  Deep Face Recognition , 2015, BMVC.

[8]  Sheldon H. Jacobson,et al.  The impact of aviation checkpoint queues on optimizing security screening effectiveness , 2011, Reliab. Eng. Syst. Saf..

[9]  Sheldon H. Jacobson,et al.  Optimal Aviation Security Screening Strategies With Dynamic Passenger Risk Updates , 2012, IEEE Transactions on Intelligent Transportation Systems.

[10]  R. Yager On the dempster-shafer framework and new combination rules , 1987, Inf. Sci..

[11]  Terje Aven,et al.  Foundational Issues in Risk Assessment and Risk Management , 2012, Risk analysis : an official publication of the Society for Risk Analysis.

[12]  Baozhi Chen,et al.  Research challenges in computation, communication, and context awareness for ubiquitous healthcare , 2012, IEEE Communications Magazine.

[13]  A. Lakshmi,et al.  DEEP REPRESENTATIONS FOR IRIS , FACE , AND FINGERPRINT SPOOFING DETECTION , 2017 .

[14]  Anil K. Jain,et al.  Bridging the gap: from biometrics to forensics , 2015, Philosophical Transactions of the Royal Society B: Biological Sciences.

[15]  Li Lin,et al.  Passenger grouping with risk levels in an airport security system , 2009, Eur. J. Oper. Res..

[16]  Sheng-Min Wang,et al.  Symptom severity of panic disorder associated with impairment in emotion processing of threat‐related facial expressions , 2013, Psychiatry and clinical neurosciences.

[17]  Luc Van Gool,et al.  Random Forests for Real Time 3D Face Analysis , 2012, International Journal of Computer Vision.

[18]  Patrick J. Grother,et al.  Face Recognition Vendor Test (FRVT) Performance of Face Identification Algorithms NIST IR 8009 , 2014 .

[19]  Brendan J. Frey,et al.  A comparison of algorithms for inference and learning in probabilistic graphical models , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[20]  James Philbin,et al.  FaceNet: A unified embedding for face recognition and clustering , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[21]  Francois Capman,et al.  Embedded security system for multi-modal surveillance in a railway carriage , 2015, SPIE Security + Defence.

[22]  Xiaogang Wang,et al.  Deep Convolutional Network Cascade for Facial Point Detection , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[23]  Josef Kittler,et al.  Quality-Based Score Normalization With Device Qualitative Information for Multimodal Biometric Fusion , 2010, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[24]  Ronald R. Yager,et al.  Including probabilistic uncertainty in fuzzy logic controller modeling using Dempster-Shafer theory , 1995, IEEE Trans. Syst. Man Cybern..

[25]  Hugo Proença,et al.  Soft Biometrics: Globally Coherent Solutions for Hair Segmentation and Style Recognition Based on Hierarchical MRFs , 2017, IEEE Transactions on Information Forensics and Security.

[26]  Jeffrey F. Cohn,et al.  Automatic detection of pain intensity , 2012, ICMI '12.

[27]  George W. Quinn,et al.  Face In Video Evaluation (FIVE) Face Recognition of Non-Cooperative Subjects , 2017 .

[28]  Anthony J. Palmer,et al.  Approach for selecting the most suitable Automated Personal Identification Mechanism (ASMSA) , 2010, Comput. Secur..

[29]  Mark S. Nixon,et al.  Targeted impersonation as a tool for the detection of biometric system vulnerabilities , 2013, 2013 IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS).

[30]  Jay F. Nunamaker,et al.  Embodied Conversational Agent-Based Kiosk for Automated Interviewing , 2011, J. Manag. Inf. Syst..

[31]  Andy Harter,et al.  Parameterisation of a stochastic model for human face identification , 1994, Proceedings of 1994 IEEE Workshop on Applications of Computer Vision.

[32]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[33]  Sridha Sridharan,et al.  Score-Level Multibiometric Fusion Based on Dempster–Shafer Theory Incorporating Uncertainty Factors , 2015, IEEE Transactions on Human-Machine Systems.

[34]  Anthony J. Palmer,et al.  Criteria to evaluate Automated Personal Identification Mechanisms , 2008, Comput. Secur..

[35]  Youn Chul Choi,et al.  Analytic Hierarchy Process Approach for Identifying Relative Importance of Factors to Improve Passenger Security Checks at Airports , 2006 .

[36]  Shawn Eastwood,et al.  Bridging the Gap Between Forensics and Biometric-Enabled Watchlists for e-Borders , 2017, IEEE Computational Intelligence Magazine.

[37]  Marco Grangetto,et al.  Kinect-based gait analysis for automatic frailty syndrome assessment , 2016, 2016 IEEE International Conference on Image Processing (ICIP).

[38]  Thomas Vetter,et al.  Face Recognition Based on Fitting a 3D Morphable Model , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[39]  Shawn Eastwood,et al.  Risk profiler in automated human authentication , 2014, 2014 IEEE Symposium on Computational Intelligence for Engineering Solutions (CIES).

[40]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[41]  Philippe Weber,et al.  Bayesian networks inference algorithm to implement Dempster Shafer theory in reliability analysis , 2008, Reliab. Eng. Syst. Saf..

[42]  Luis M. de Campos,et al.  Probability Intervals: a Tool for uncertain Reasoning , 1994, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[43]  Richa Singh,et al.  Face Verification via Learned Representation on Feature-Rich Video Frames , 2017, IEEE Transactions on Information Forensics and Security.

[44]  Daniel Cuesta Cantarero,et al.  A Multi-modal Biometric Fusion Implementation for ABC Systems , 2013, 2013 European Intelligence and Security Informatics Conference.

[45]  Ajmal S. Mian,et al.  Using Kinect for face recognition under varying poses, expressions, illumination and disguise , 2013, 2013 IEEE Workshop on Applications of Computer Vision (WACV).

[46]  Yongsheng Gao,et al.  Face recognition across pose: A review , 2009, Pattern Recognit..

[47]  Dmitry O. Gorodnichy,et al.  Biometric-Enabled Authentication Machines: A Survey of Open-Set Real-World Applications , 2016, IEEE Transactions on Human-Machine Systems.

[48]  Anil K. Jain,et al.  Unconstrained Face Recognition: Identifying a Person of Interest From a Media Collection , 2014, IEEE Transactions on Information Forensics and Security.

[49]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[50]  Norman Poh,et al.  Biometric system design under zero and non-zero effort attacks , 2013, 2013 International Conference on Biometrics (ICB).

[51]  Christian Oberli,et al.  Performance Evaluation of UHF RFID Technologies for Real-Time Passenger Recognition in Intelligent Public Transportation Systems , 2010, IEEE Transactions on Intelligent Transportation Systems.

[52]  Marvin Rausand,et al.  Risk Assessment: Theory, Methods, and Applications , 2011 .

[53]  Sridha Sridharan,et al.  Automatically Detecting Pain in Video Through Facial Action Units , 2011, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[54]  Anderson Rocha,et al.  Learning for Meta-Recognition , 2012, IEEE Transactions on Information Forensics and Security.

[55]  Luc Van Gool,et al.  Real time head pose estimation with random regression forests , 2011, CVPR 2011.

[56]  Timothy F. Cootes,et al.  Active Shape Models-Their Training and Application , 1995, Comput. Vis. Image Underst..

[57]  Sheldon H. Jacobson,et al.  Risk-Based Policies for Airport Security Checkpoint Screening , 2010, Transp. Sci..

[58]  Pascal Vincent,et al.  Representation Learning: A Review and New Perspectives , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[59]  Marina L. Gavrilova,et al.  Situational Awareness through Biometrics , 2013, Computer.

[60]  Stephen Coulthart Using social media for global security , 2016 .

[61]  Anil K. Jain,et al.  Soft Biometric Traits for Continuous User Authentication , 2010, IEEE Transactions on Information Forensics and Security.

[62]  Jeffrey F. Cohn,et al.  Painful data: The UNBC-McMaster shoulder pain expression archive database , 2011, Face and Gesture 2011.

[63]  Timothy F. Cootes,et al.  Active Appearance Models , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[64]  Peter Robinson,et al.  3D Constrained Local Model for rigid and non-rigid facial tracking , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[65]  Vincenzo Piuri,et al.  Biometric Recognition in Automated Border Control , 2016, ACM Comput. Surv..

[66]  Fella Hachouf,et al.  Score-Level Fusion of Face and Voice Using Particle Swarm Optimization and Belief Functions , 2015, IEEE Transactions on Human-Machine Systems.

[67]  Svetlana N. Yanushkevich,et al.  Multi-spectral facial biometrics in access control , 2014, 2014 IEEE Symposium on Computational Intelligence in Biometrics and Identity Management (CIBIM).

[68]  Didier Bigo,et al.  Justice and Home Affairs databases and a Smart Borders System at EU external borders. An evaluation of current and forthcoming proposals. CEPS Paper in Liberty and Security No. 52/December 2012 , 2012 .

[69]  Carlos D. Castillo,et al.  Using Stereo Matching for 2-D Face Recognition Across Pose , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[70]  Jean Dezert,et al.  UNM Digital Repository UNM Digital Repository Fusion of Sources of Evidence with Different Importances and Fusion of Sources of Evidence with Different Importances and Reliabilities Reliabilities , 2022 .

[71]  Shawn Eastwood,et al.  Taxonomy and Modeling of Impersonation in e-Border Authentication , 2015, 2015 Sixth International Conference on Emerging Security Technologies (EST).

[72]  M. Turk,et al.  Eigenfaces for Recognition , 1991, Journal of Cognitive Neuroscience.

[73]  Thomas B. Moeslund,et al.  Pain recognition using spatiotemporal oriented energy of facial muscles , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[74]  Dario Pompili,et al.  A Distributed Computing Framework for Real-Time Detection of Stress and of Its Propagation in a Team , 2016, IEEE Journal of Biomedical and Health Informatics.

[75]  Shawn Eastwood,et al.  Risk Assessment in Authentication Machines , 2016, Recent Advances in Computational Intelligence in Defense and Security.

[76]  David A. Atkinson,et al.  Technology Gap Analysis for the Detection of Process Signatures Using Less Than Remote Methods , 2005 .