Automatic 3D Facial Expression Analysis in Videos

We introduce a novel framework for automatic 3D facial expression analysis in videos. Preliminary results demonstrate editing facial expression with facial expression recognition. We first build a 3D expression database to learn the expression space of a human face. The real-time 3D video data were captured by a camera/projector scanning system. From this database, we extract the geometry deformation independent of pose and illumination changes. All possible facial deformations of an individual make a nonlinear manifold embedded in a high dimensional space. To combine the manifolds of different subjects that vary significantly and are usually hard to align, we transfer the facial deformations in all training videos to one standard model. Lipschitz embedding embeds the normalized deformation of the standard model in a low dimensional generalized manifold. We learn a probabilistic expression model on the generalized manifold. To edit a facial expression of a new subject in 3D videos, the system searches over this generalized manifold for optimal replacement with the 'target' expression, which will be blended with the deformation in the previous frames to synthesize images of the new expression with the current head pose. Experimental results show that our method works effectively

Unscented Klt: Nonlinear Feature And Uncertainty Tracking

Accurate feature tracking is the foundation of several high level tasks, such as 3D reconstruction and motion analysis. Although there are many feature tracking algorithms, most of them do not maintain information about the error of the data being tracked. In this paper, we propose a new generic framework that uses the Scaled Unscented Transform (SUT) to augment arbitrary feature tracking algorithms, by introducing Gaussian Random Variables (GRV) for the representation of features' locations uncertainties. Here, we apply the framework to the wellunderstood Kanade-Lucas-Tomasi (KLT) feature tracker, giving birth to what we call Unscented KLT (UKLT). It tracks probabilistic confidences and better rejects errors, all on-line, and leads to more robust computer vision applications. We also validade the experiments with a bundle adjustment procedure, using real and synthetic sequences. © 2006 IEEE.187193Chowdhury, A.K.R., (2002) Statistical Analysis of 3D Modeling from Monocular Video Streams, , PhD thesis, University of MarylandFischler, M., Bolles, R., Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography (1981) Communications of the ACM, 24 (6), pp. 381-395Fusiello, A., Trucco, E., Tommasini, T., Roberto, V., Improving feature tracking with robust statistics (1999) Pattern Analysis and Applications, 2, pp. 312-320Goldenstein, S., A gentle introduction to predictive filters (2004) Revista de Informatica Teórica e Aplicada (RITA), 11 (1), pp. 61-89. , OctHager, G., Belhumeur, P., Efficient region tracking with parametric models of geometry and illumination (1998) PAMI, 20, pp. 1025-1039Jin, H., Favaro, P., Soatto, S., Real-time feature tracking and outlier rejection with changes in illumination (2001) ICCV, pp. 684-689Julier, S., Uhlmann, J., A new extension of the kalman filter to nonlinear systems (1997) In SPIEKanazawa, Y., Kanatani, K., Do we really have to consider covariance matrices for image features? (2001) ICCV, pp. 301-306Lowe, D., Distinctive image features from scale-invariant keypoints (2004) IJCV, 60 (2), pp. 91-110Lucas, B., Kanade, T., An iterative image registration technique with an application to stereo vision (1981) IJCAI81, pp. 674-679Ma, Y., Soatto, S., Kosecka, J., Sastry, S., (2004) An Invitation to 3D Vision - From Images to Geometric Models, , SpringerShi, J., Tomasi, C., Good features to track (1994) CVPR, pp. 593-600Simoncelli, E., (1999) Handbook of Computer Vision and Applications, 2, pp. 397-422. , chapter Bayesian Multi-scale Differential Optical Flow, Academic PressSteele, P.M., Jaynes, C., Feature uncertainty arising from covariant image noise (2005) CVPR, 1, pp. 1063-1070Tomasi, C., Kanade, T., Detection and tracking of point features (1991), Technical Report CMU-CS-91-132, Carnegie Mellon University, AprilTorr, P., Zisserman, A., S., M., Robust detection of degeneracy (1995) ICCV, pp. 1037-1044Triggs, B., McLauchlan, P., Hartley, R., Fitzgibbon, A., Bundle adjustment - a modern synthesis (1999) Proceedings of the International Workshop on Vision Algorithms: Theory and Practice, pp. 298-372van der Merwe, R., Doucet, A., de Freitas, N., Wan, E., The unscented particle filter (2000), Technical Report CUED/FINFENG/TR380, Cambridge University, AugustWan, E., van der Merwe, R., (2001) Kalman Filtering and Neural Networks, , Wiley PublishingZhang, Z., Determining the epipolar geometry and its uncertainty: A review (1998) IJCV, 27 (2), pp. 161-198Zhu, J., Schwartz, S., Liu, B., Object tracking: Feature selection and confidence propagation (2004) CRV, pp. 18-2

An Approach To The Correlation Of Security Events Based On Machine Learning Techniques

Organizations face the ever growing challenge of providing security within their IT infrastructures. Static approaches to security, such as perimetral defense, have proven less than effective - and, therefore, more vulnerable - in a new scenario characterized by increasingly complex systems and by the evolution and automation of cyber attacks. Moreover, dynamic detection of attacks through IDSs (Instrusion Detection Systems) presents too many false positives to be effective. This work presents an approach on how to collect and normalize, as well as how to fuse and classify, security alerts. This approach involves collecting alerts from different sources and normalizes them according to standardized structures - IDMEF (Intrusion Detection Message Exchange Format). The normalized alerts are grouped into meta-alerts (fusion, or clustering), which are later classified using machine learning techniques into attacks or false alarms. We validate and report an implementation of this approach against the DARPA Challenge and the Scan of the Month, using three different classifications - SVMs, Bayesian Networks and Decision Trees - having achieved high levels of attack detection with little false positives. Our results also indicate that our approach outperforms other works when it comes to detecting new kinds of attacks, making it more suitable to a world of evolving attacks. © 2013 Stroeh et al.41116Joosen, W., Lagaisse, B., Truyen, E., Handekyn, K., Towards application driven security dashboards in future middleware (2012) J Internet Serv Appl, 3, pp. 107-115. , 10.1007/s13174-011-0047-6Hale, J., Brusil, P., Secur(e/ity) management: A continuing uphill climb (2007) J Netw Syst Manage, 15 (4), pp. 525-553Ganame, A.K., Bourgeois, J., Bidou, R., Spies, F., A global security architecture for intrusion detection on computer networks (2008) Elsevier Comput Secur, 27, pp. 30-47Giacinto, G., Perdisci, R., Roli, F., (2005) Alarm Clustering For Intrusion Detection Systems In Computer Networks, 19, pp. 429-438. , In: Perner P, Imiya A (eds)Ning, P., Cui, Y., Reeves, D.S., Xu, D., Techniques and tools for analyzing intrusion alerts (2004) ACM Trans Inf Syst Secur (TISSEC), 7, pp. 274-318Boyer, S., Dain, O., Cunningham, R., Stellar: A fusion system for scenario construction and security risk assessment (2005) Proceedings of the Third IEEE International Workshop On Information Assurance, pp. 105-116. , IEEE Computer SocietyJulisch, K., Clustering intrusion detection alarms to support root cause analysis (2003) ACM Trans Inf Syst Security, 6, pp. 443-471Liu, P., Zang, W., Yu, M., Incentive-based modeling and inference of attacker intent, objectives, and strategies (2005) ACM Trans Inf Syst Secur (TISSEC), 8, pp. 78-118Sabata, B., Evidence aggregation in hierarchical evidential reasoning (2005) UAI Applications Workshop, Uncertainty In AI 2005, , Edinburgh, ScotlandChyssler, T., Burschka, S., Semling, M., Lingvall, T., Burbeck, K., Alarm reduction and correlation in intrusion detection systems (2004) Detection of Intrusions and Malware & Vulnerability Assessment Workshop (DIMVA), pp. 9-24. , Dortmund, DeutschlandOhta, S., Kurebayashi, R., Kobayashi, K., Minimizing false positives of a decision tree classifier for intrusion detection on the internet (2008) J Netw Syst Manage, 16, pp. 399-419Haines, J.W., Lippmann, R.P., Fried, D.J., Tran, E., Boswell, S., Zissman, M.A., The 1999 darpa off-line intrusion detection evaluation (2000) Comput Netw Int J Comput Telecommunications Netw, 34, pp. 579-595Project, T.H., (2004) Know Your Enemy: Learning About Security Threats, , (2nd Edition). Addison-Wesley ProfessionalSommer, R., Paxson, V., Outside the closed world: On using machine learning for network intrusion detection (2010) Proceedings of the IEEE Symposium On Security and PrivacyBowen, T., Chee, D., Segal, M., Sekar, R., Shanbhag, T., Uppuluri, P., Building survivable systems: An integrated approach based on intrustion detection and damage containment (2000) DARPA Information Survivability Conference (DISCEX)Vigna, G., Eckmann, S.T., Kemmerer, R.A., The stat tool suite (2000) Proceedings of DISCEX 2000, , Hilton Head, IEEE Computer Society PressLee, W., Stolfo, S.J., Chan, P.K., Eskin, E., Fan, W., Miller, M., Hershkop, S., Zhang, J., Real time data mining-based intrusion detection (2001) Proc. Second DARPA Information Survivability Conference and Exposition, pp. 85-100. , Anaheim, USANeumann, P.G., Porras, P.A., Experience with EMERALD to date (2005) Proceedings 1st USENIX Workshop On Intrusion Detection and Network Monitoring, pp. 73-80. , Santa Clara, CA, USAGrimaila, M., Myers, J., Mills, R., Peterson, G., Design and analysis of a dynamically configured log-based distributed security event detection methodology (2011) J Defense Model Simul: Appl Methodolgy Tech, pp. 1-23Rieke, R., Stoynova, Z., Predictive security analysis for eventdriven processes (2010) MMM-ACNS'10 Proceedings of the 5th International Conference On Mathematical Methods, models and architectures for computer network securityValdes, A., Skinner, K., Probabilistic alert correlation (2001) Proceedings of the 4th International Symposium On Recent Advances In Intrusion Detection (RAID 2001), pp. 54-68. , Davis, CA, USAAsif-Iqbal, H., Udzir, N.I., Mahmod, R., Ghani, A.A.A., Filtering events using clustering in heterogeneous security logs (2011) Inf Technol J, 10, pp. 798-806Corona, I., Giacinto, G., Mazzariello, C., Roli, F., Sansone, C., Information fusion for computer security: State of the art and open issues (2011) Inf Fusion, 10, pp. 274-284Burroughs, D.J., Wilson, L.F., Cybenko, G.V., Analysis of distributed intrusion detection systems using bayesian methods (2002) Proceedings of IEEE International Performance Computing and Communication Conference, pp. 329-334. , Phoenix, AZ, USASabata, B., Ornes, C., Multisource evidence fusion for cyber-situation assessment (2006) Proc. SPIE, 6242. , (Apr. 18, 2006). Orlando, FL, USAEndsley, M.R., Toward a theory of situation awareness in dynamic systems (1995) Human Factors: J Human Factor Ergon Soc, 37, pp. 32-64Debar, H., Curry, D., Feinstein, B., The intrusion detection message exchange format (idmef) (2007) Internet Experimental RFC, p. 4765. , http://tools.ietf.org/html/rfc4765, Available atLan, F., Chunlei, W., Guoqing, M., A framework for network security situation awareness based on knowledge discovery (2010) Computer Engineering and Technology (ICCET)Cox, K., Gerg, C., (2004) Managing Security With Snort and IDS Tools, , O'Reilly Media, SebastopolAlfedaghi, S., Mahdi, F., Events classification in log audit (2010) Int J Netw Secur Appl (IJNSA), 2, pp. 58-73Valdes, A., Skinner, K., International, S., Adaptive, model-based monitoring for cyber attack detection (2000) Recent Advances In Intrusion Detection (RAID 2000), pp. 80-92. , Springer-VerlagMahoney, M.V., Chan, P.K., Learning nonstationary models of normal network traffic for detecting novel attacks (2002) Proceedings of the Eighth ACM SIGKDD International Conference On Knowledge Discovery and Data Mining, pp. 376-385. , ACMMukkamala, S., Sung, A.H., Abraham, A., Intrusion detection using ensemble of soft computing (2003) Paradigms, Advances in Soft Computing, pp. 239-248. , Springer VerlagFaraoun, K.M., Boukelif, A., Securing network traffic using genetically evolved transformations (2006) Malays J Comput Sci, 19 (1), pp. 9-28. , (ISSN 0127-9084)Faraoun, K.M., Boukelif, A., Neural networks learning improvement using the k-means clustering algorithm to detect network intrusions (2006) Int J Comput Intell Appl, 6 (1), pp. 77-99Tandon, G., Chan, P., Learning rules from system call arguments and sequences for anomaly detection (2003) ICDM Workshop On Data Mining For Computer Security (DMSEC), pp. 20-29. , Melbourne, FL, USAMukkamala, S., Sung, A.H., Feature ranking and selection for intrusion detection systems using support vector machines (2002) Proceedings of the Second Digital Forensic Research WorkshopChang, C.C., Lin, C.J., (2001) LIBSVM: A Library For Support Vector Machines, , http://www.csie.ntu.edu.tw/cjlin/libsvm, Available atHsu, W.C., Chang, C.C., Lin, J.C., (2007) A Practical Guide to Support Vector Classification, , http://www.csie.ntu.edu.tw/cjlin, tech. rep., Department of Computer Science, National Taiwan University. Available atWitten, I.H., Frank, E., (2000) Data Mining: Practical Machine Learning Tools and Techniques, , (Second Edition), Morgan KaufmannKayacik, H.G., Zincir-Heywood, A.N., (2003) Using Intrusion Detection Systems With a Firewall: Evaluation On Darpa 99 Dataset, , Tech. rep., NIMS Technical Report 06200

Probabilistic Multiagent Patrolling

Patrolling refers to the act of walking around an area, with some regularity, in order to protect or supervise it. A group of agents is usually required to perform this task efficiently. Previous works in this field, using a metric that minimizes the period between visits to the same position, proposed static solutions that repeats a cycle over and over. But an efficient patrolling scheme requires unpredictability, so that the intruder cannot infer when the next visitation to a position will happen. This work presents various strategies to partition the sites among the agents, and to compute the visiting sequence. We evaluate these strategies using three metrics which approximates the probability of averting three types of intrusion - a random intruder, an intruder that waits until the guard leaves the site to initiate the attack, and an intruder that uses statistics to forecast how long the next visit to the site will be. We present the best strategies for each of these metrics, based on 500 simulations. © 2008 Springer Berlin Heidelberg.5249 LNAI124133Hespanha, J., Kim, H.J., Sastry, S., Multiple-agent probabilistic pursuit-evasion games (1999) Proceedings of the 38th IEEE Conference on Decision and Control, 3, pp. 2432-2437Flint, M., Polycarpou, M., Fernandez-Gaucherand, E., Cooperative control for multiple autonomous uav's searching for targets (2002) Proceedings of the 41st IEEE Conference on Decision and Control, 3, pp. 2823-2828Subramanian, S., Cruz, J.: Adaptive models of pop-up threats for multi-agent persistent area denial. In: Proceedings. 42nd IEEE Conference on Decision and Control, 2003, December 9-12, 1, pp. 510-515 (2003)Grace, J., Baillieul, J., Stochastic strategies for autonomous robotic surveillance (2005) 44th IEEE Conference on Decision and Control, 2005 and 2005 European Control Conference. CDC-ECC, pp. 2200-2205Machado, A., Ramalho, G., Zucker, J.D., Drogoul, A.: Multi-agent patrolling: An empirical analysis of alternative architectures. In: Sichman, J.S., Bousquet, F., Davidsson, P. (eds.) MABS 2002. LNCS (LNAI), 2581, pp. 155-170. Springer, Heidelberg (2003)Chevaleyre, Y., Sempe, F., Ramalho, G., A theoretical analysis of multi-agent patrolling strategies (2004) AAMAS 2004: Proceedings of the 3rd International Conference on Autonomous Agents and Multiagent Systems, 3, pp. 1524-1525. , IEEE Computer Society, Los AlamitosAlmeida, A., Ramalho, G., Santana, H., Tedesco, P.A., Menezes, T., Comible, V., Chevaleyre, Y.: Recent advances on multi-agent patrolling. In: Bazzan, A.L.C, Labidi, S. (eds.) SBIA 2004. LNCS (LNAI), 3171, pp. 474-483. Springer, Heidelberg (2004)Santana, H., Ramalho, G., Corruble, V., Ratitch, B., Multi-agent patrolling with reinforcement learning (2004) AAMAS 2004: Proceedings of the 3rd International Conference on Autonomous Agents and Multiagent Systems, 3, pp. 1122-1129. , IEEE Computer Society, Los AlamitosMenezes, T., Tedesco, P., Ramalho, G., Negotiator agents for the patrolling task (2006) LNCS (LNAI, 4140, pp. 48-57. , Sichman, J.S, Coelho, H, Rezende, S.O, eds, IBERAMIA 2006 and SBIA 2006, Springer, HeidelbergBarnes, G., Feige, U., Short random walks on graphs (1996) SIAM Journal on Discrete Mathematics, 9 (1), pp. 19-28Jain, A.K., Murty, M.N., Flynn, P.J., Data clustering: A review (1999) ACM Computing Surveys, 31 (3), pp. 264-323Volgenant, A., Jonker, R., A branch and bound algorithm for the symmetric traveling salesman problem based on the 1-tree relaxation (1982) European Journal of Operational Research, 9, pp. 83-89Karypis, G., Kumar, V., A fast and high quality multilevel scheme for partitioning irregular graphs (1998) SIAM J. Sci. Comput, 20 (1), pp. 359-39

Using Unsupervised Learning For Graph Construction In Semi-supervised Learning With Graphs

Semi-supervised Learning with Graphs can achieve good results in classification tasks even in difficult conditions. Unfortunately, it can be slow and use a lot of memory. The first important step of the graph-based semi-supervised learning approaches is the construction of the graph from the data, where each data-point usually becomes a vertex in the graph - a potential problem with large amounts of data. In this paper, we present a graph construction method that uses an unsupervised neural network called growing neural gas (GNG). The GNG instance presents a intelligent stopping criteria that determines when the final network configuration maps correctly the input-data points. With that in mind, we use the final trained network as a reduced input graph for the semi-supervised classification algorithm, associating original data-points to the neurons they have activated in the unsupervised training process. © 2013 IEEE.2430Zhu, X., (2005) Semi-supervised Learning with Graphs, , Ph.D. dissertation, Carnegie Mellon UniversityZhu, X., Goldberg, A.B., (2009) Introduction to Semi-Supervised Learning, , Morgan & Claypool PublishersZhu, X., Semi-supervised learning literature survey (2008) SurveyFergus, R., Weiss, Y., Torralba, A., Semi-supervised learning in gigantic image collections (2009) Advances in Neural Information Processing Systems (NIPS)Fritzke, B., A growing neural gas network learns topologies (1995) Advances in Neural Information Processing Systems 7. MIT Press, pp. 625-632Graham, J., Starzyk, J., A hybrid self-organizing neural gas based network (2008) Neural Networks 2008. IJCNN 2008. (IEEE World Congress on Computational Intelligence). IEEE International Joint Conference on, pp. 3806-3813Sledge, I., Keller, J., Growing neural gas for temporal clustering (2008) Pattern Recognition, 2008. ICPR 2008. 19th International Conference on, pp. 1-4Blum, A., Lafferty, J., Rwebangira, M.R., Reddy, R., Semisupervised learning using randomized mincuts (2004) Proceedings of the 21st International Conference on Machine Learning, pp. 97-104Talwalkar, A., Kumar, S., Rowley, H., Large-scale manifold learning (2008) Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on, pp. 1-8Mahdaviani, M., De Freitas, N., Fraser, B., Hamze, F., Fast computational methods for visually guided robots (2005) ICRA'05, pp. 138-143Zhu, X., Lafferty, J., Harmonic mixtures: Combining mixture models and graph-based methods for inductive and scalable semi-supervised learning (2005) Proc. Int. Conf. Machine Learning. ACM Press, pp. 1052-1059Chavez, D., Laures, G., Loayza, K., Patino, R., A stopping criteria for the growing neural gas based on a validity separation index for clusters (2011) Hybrid Intelligent Systems (HIS), 2011 11th International Conference on, pp. 578-583Zhu, X., Ghahramani, Z., Lafferty, J., Semi-supervised learning using gaussian fields and harmonic functions (2003) ICML, pp. 912-919Krizhevsky, A., Learning multiple layers of features from tiny images (2009) Master's Thesis, , http://www.cs.toronto.edu/kriz/learning-features-2009-TR.pdfTorralba, A., Fergus, R., Freeman, W.T., 80 million tiny images: A large data set for nonparametric object and scene recognition (2008) IEEE Trans. Pattern Anal. Mach. Intell., 30 (11), pp. 1958-1970. , http://dx.doi.org/10.1109/TPAMI.2008.128, NovOliva, A., Torralba, A., Modeling the shape of the scene: A holistic representation of the spatial envelope (2001) International Journal of Computer Vision, 42, pp. 145-17

Facial Fiducial Points Detection Using Discriminative Filtering On Principal Components

The Discriminative Filtering technique performs pattern recognition using a two-dimensional filter. It has a closed-form design, based on the pattern and the statistics of the image set. Here, we investigate the use of Discriminative Filtering for detecting fiducial points in human faces. We show that designing discriminative filters for the principal components increases robustness. The method is assessed in a fiducial points detection framework using a Gentle AdaBoost classifier. © 2010 IEEE.26812684Nandy, D., Ben-Arie, J., EXM eigen templates for detecting and classifying arbitrary junctions (1998) Proceedings of the International Conference on Image Processing, pp. 211-215. , Kobe, Japan, OctoberMendonça, A.P., Da Silva, E.A.B., Multiple template detection using impulse restoration and discriminative filters (2003) IEE Electronics Letters, 39 (16), pp. 1172-1174. , AugustMendonça, A.P., Da Silva, E.A.B., Two-dimensional discriminative filters for image template detection (2001) Proceedings of the International Conference on Image Processing, , Thessaloniki, Greece, SeptemberCootes, T.F., Taylor, C.J., Cooper, D.H., Graham, J., Active shape models-their training and application (1995) Computer Vision and Image Understanding, 61 (1), pp. 38-59Stefano, A., Paola, C., Raffaella, L., An efficient method to detect facial fiducial points for face recognition (2004) Proceedings of the 17th International Conference on Pattern Recognition, pp. 532-535. , Cambridge, UK, August(2008) The BioID Database, , http://www.humanscan.de/support/downloads/facedb.phpJoachims, T., Burges, C., Scholkopf, B., Smola, A., (1999) Advances in Kernel Methods: Support Vector Learning, pp. 169-184. , Eds., chapter Making large-scale support vector machine learning practical, MIT press, Cambridge, MAMendonça, A.P., Da Silva, E.A.B., Closed-form solutions for discriminative filtering using impulse restoration techniques (2002) IEE Electronics Letters, 38 (22), pp. 1332-1333. , OctoberNaser, A.A., Galatsanos, N.P., Wernick, M.N., Impulse restoration-based template-matching using the expectation-maximization algorithm (1997) Proceedings of the International Conference on Image Processing, pp. 158-161. , Washington, DC, OctoberNaser, A.A., (2000) Impulse Restoration-based Template-matching, , Ph.D. Dissertation, University of Illinois, Chicago, USAKirby, M., Sirovich, L., Application of the karhunen-loeve procedure for the characterization of human faces (1990) IEEE Transactions on Pattern Analysis and Machine Intelligence, 12Stan, Z.L., Anil, K.J., (2004) Handbook of Face Recognition, , Springer-Verlag, Secaucus, NJ, USA, 1ed editionViola, P., Jones, M., Robust real-time object detection (2001) International Journal of Computer Vision, 57 (2), pp. 137-154. , JulyXiaoy, T., Triggs, B., Enhanced local texture feature sets for face recognition under difficult lighting conditions (2007) Proceedings of the International Workshop on Analysis and Modeling of Faces and Gestures, pp. 168-182Jerome, F., Trevor, H., Tibshirani, R., Additive logistic regression: A statistical view of boosting (1998) Annals of Statistics, 28, p. 2000(2008) The GML AdaBoost Matlab Toolbox, , http://graphics.cs.msu.ru/en/science/research/machinelearning/ adaboosttoolbo