Evolving Large-Scale Data Stream Analytics based on Scalable PANFIS

Many distributed machine learning frameworks have recently been built to speed up the large-scale data learning process. However, most distributed machine learning used in these frameworks still uses an offline algorithm model which cannot cope with the data stream problems. In fact, large-scale data are mostly generated by the non-stationary data stream where its pattern evolves over time. To address this problem, we propose a novel Evolving Large-scale Data Stream Analytics framework based on a Scalable Parsimonious Network based on Fuzzy Inference System (Scalable PANFIS), where the PANFIS evolving algorithm is distributed over the worker nodes in the cloud to learn large-scale data stream. Scalable PANFIS framework incorporates the active learning (AL) strategy and two model fusion methods. The AL accelerates the distributed learning process to generate an initial evolving large-scale data stream model (initial model), whereas the two model fusion methods aggregate an initial model to generate the final model. The final model represents the update of current large-scale data knowledge which can be used to infer future data. Extensive experiments on this framework are validated by measuring the accuracy and running time of four combinations of Scalable PANFIS and other Spark-based built in algorithms. The results indicate that Scalable PANFIS with AL improves the training time to be almost two times faster than Scalable PANFIS without AL. The results also show both rule merging and the voting mechanisms yield similar accuracy in general among Scalable PANFIS algorithms and they are generally better than Spark-based algorithms. In terms of running time, the Scalable PANFIS training time outperforms all Spark-based algorithms when classifying numerous benchmark datasets.Comment: 20 pages, 5 figure

Performance analysis of multimodal biometric fusion

Biometrics is constantly evolving technology which has been widely used in many official and commercial identification applications. In fact in recent years biometric-based authentication techniques received more attention due to increased concerns in security. Most biometric systems that are currently in use typically employ a single biometric trait. Such systems are called unibiometric systems. Despite considerable advances in recent years, there are still challenges in authentication based on a single biometric trait, such as noisy data, restricted degree of freedom, intra-class variability, non-universality, spoof attack and unacceptable error rates. Some of the challenges can be handled by designing a multimodal biometric system. Multimodal biometric systems are those which utilize or are capable of utilizing, more than one physiological or behavioural characteristic for enrolment, verification, or identification. In this thesis, we propose a novel fusion approach at a hybrid level between iris and online signature traits. Online signature and iris authentication techniques have been employed in a range of biometric applications. Besides improving the accuracy, the fusion of both of the biometrics has several advantages such as increasing population coverage, deterring spoofing activities and reducing enrolment failure. In this doctoral dissertation, we make a first attempt to combine online signature and iris biometrics. We principally explore the fusion of iris and online signature biometrics and their potential application as biometric identifiers. To address this issue, investigations is carried out into the relative performance of several statistical data fusion techniques for integrating the information in both unimodal and multimodal biometrics. We compare the results of the multimodal approach with the results of the individual online signature and iris authentication approaches. This dissertation describes research into the feature and decision fusion levels in multimodal biometrics.State of Kuwait – The Public Authority of Applied Education and Trainin

Calibration-free Pedestrian Partial Pose Estimation Using a High-mounted Kinect

Les applications de l’analyse du comportement humain ont subit de rapides développements durant les dernières décades, tant au niveau des systèmes de divertissements que pour des applications professionnelles comme les interfaces humain-machine, les systèmes d’assistance de conduite automobile ou des systèmes de protection des piétons. Cette thèse traite du problème de reconnaissance de piétons ainsi qu’à l’estimation de leur orientation en 3D. Cette estimation est faite dans l’optique que la connaissance de cette orientation est bénéfique tant au niveau de l’analyse que de la prédiction du comportement des piétons. De ce fait, cette thèse propose à la fois une nouvelle méthode pour détecter les piétons et une manière d’estimer leur orientation, par l’intégration séquentielle d’un module de détection et un module d’estimation d’orientation. Pour effectuer cette détection de piéton, nous avons conçu un classificateur en cascade qui génère automatiquement une boîte autour des piétons détectés dans l’image. Suivant cela, des régions sont extraites d’un nuage de points 3D afin de classifier l’orientation du torse du piéton. Cette classification se base sur une image synthétique grossière par tramage (rasterization) qui simule une caméra virtuelle placée immédiatement au-dessus du piéton détecté. Une machine à vecteurs de support effectue la classification à partir de cette image de synthèse, pour l’une des 10 orientations discrètes utilisées lors de l’entrainement (incréments de 30 degrés). Afin de valider les performances de notre approche d’estimation d’orientation, nous avons construit une base de données de référence contenant 764 nuages de points. Ces données furent capturées à l’aide d’une caméra Kinect de Microsoft pour 30 volontaires différents, et la vérité-terrain sur l’orientation fut établie par l’entremise d’un système de capture de mouvement Vicon. Finalement, nous avons démontré les améliorations apportées par notre approche. En particulier, nous pouvons détecter des piétons avec une précision de 95.29% et estimer l’orientation du corps (dans un intervalle de 30 degrés) avec une précision de 88.88%. Nous espérons ainsi que nos résultats de recherche puissent servir de point de départ à d’autres recherches futures.The application of human behavior analysis has undergone rapid development during the last decades from entertainment system to professional one, as Human Robot Interaction (HRI), Advanced Driver Assistance System (ADAS), Pedestrian Protection System (PPS), etc. Meanwhile, this thesis addresses the problem of recognizing pedestrians and estimating their body orientation in 3D based on the fact that estimating a person’s orientation is beneficial in determining their behavior. In this thesis, a new method is proposed for detecting and estimating the orientation, in which the result of a pedestrian detection module and a orientation estimation module are integrated sequentially. For the goal of pedestrian detection, a cascade classifier is designed to draw a bounding box around the detected pedestrian. Following this, extracted regions are given to a discrete orientation classifier to estimate pedestrian body’s orientation. This classification is based on a coarse, rasterized depth image simulating a top-view virtual camera, and uses a support vector machine classifier that was trained to distinguish 10 orientations (30 degrees increments). In order to test the performance of our approach, a new benchmark database contains 764 sets of point cloud for body-orientation classification was captured. For this benchmark, a Kinect recorded the point cloud of 30 participants and a marker-based motion capture system (Vicon) provided the ground truth on their orientation. Finally we demonstrated the improvements brought by our system, as it detected pedestrian with an accuracy of 95:29% and estimated the body orientation with an accuracy of 88:88%.We hope it can provide a new foundation for future researches

Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning

Learning-based pattern classifiers, including deep networks, have shown impressive performance in several application domains, ranging from computer vision to cybersecurity. However, it has also been shown that adversarial input perturbations carefully crafted either at training or at test time can easily subvert their predictions. The vulnerability of machine learning to such wild patterns (also referred to as adversarial examples), along with the design of suitable countermeasures, have been investigated in the research field of adversarial machine learning. In this work, we provide a thorough overview of the evolution of this research area over the last ten years and beyond, starting from pioneering, earlier work on the security of non-deep learning algorithms up to more recent work aimed to understand the security properties of deep learning algorithms, in the context of computer vision and cybersecurity tasks. We report interesting connections between these apparently-different lines of work, highlighting common misconceptions related to the security evaluation of machine-learning algorithms. We review the main threat models and attacks defined to this end, and discuss the main limitations of current work, along with the corresponding future challenges towards the design of more secure learning algorithms.Comment: Accepted for publication on Pattern Recognition, 201

Automatic Classification of Seafloor Image Data by Geospatial Texture Descriptors

A novel approach for automatic context-sensitive classification of spatially distributed image data is introduced. The proposed method targets applications of seafloor habitat mapping but is generally not limited to this domain or use case. Spatial context information is incorporated in a two-stage classification process, where in the second step a new descriptor for patterns of feature class occurrence according to a generically defined classification scheme is applied. The method is based on supervised machine learning, where numerous state-of-the-art approaches are applicable. The descriptor computation originates from texture analysis in digital image processing. Patterns of feature class occurrence are perceived as a texture-like phenomenon and the descriptors are therefore denoted by Geospatial Texture Descriptors. The proposed method was extensively validated based on a set of more than 4000 georeferenced video mosaics acquired at the Haakon Mosby Mud Volcano north-west of Norway recorded during cruise ARK XIX3b of the German research vessel Polarstern. The underlying classification scheme was derived from a scheme developed for manual annotation of the same dataset applied in the course of Jerosch [2006]. Features of interest are related to methane discharge at mud volcanoes, which are considered a significant source of methane emission. In the experimental evaluation, based on the prepared training and test data, a major improvement of the classification precision compared to local classification as well as classification based on the raw data from the local spatial context was achieved by the application of the proposed method. The classification precision was particularly improved for rarely occurring classes. In a further comparison with annotated data available from Jerosch [2006] the regional setting of the investigation area obtained by the application of the proposed method was found almost equivalent to the results of an experienced scientist