Maximized Posteriori Attributes Selection from Facial Salient Landmarks for Face Recognition

This paper presents a robust and dynamic face recognition technique based on the extraction and matching of devised probabilistic graphs drawn on SIFT features related to independent face areas. The face matching strategy is based on matching individual salient facial graph characterized by SIFT features as connected to facial landmarks such as the eyes and the mouth. In order to reduce the face matching errors, the Dempster-Shafer decision theory is applied to fuse the individual matching scores obtained from each pair of salient facial features. The proposed algorithm is evaluated with the ORL and the IITK face databases. The experimental results demonstrate the effectiveness and potential of the proposed face recognition technique also in case of partially occluded faces.Comment: 8 pages, 2 figure

Face Recognition by Fusion of Local and Global Matching Scores using DS Theory: An Evaluation with Uni-classifier and Multi-classifier Paradigm

Faces are highly deformable objects which may easily change their appearance over time. Not all face areas are subject to the same variability. Therefore decoupling the information from independent areas of the face is of paramount importance to improve the robustness of any face recognition technique. This paper presents a robust face recognition technique based on the extraction and matching of SIFT features related to independent face areas. Both a global and local (as recognition from parts) matching strategy is proposed. The local strategy is based on matching individual salient facial SIFT features as connected to facial landmarks such as the eyes and the mouth. As for the global matching strategy, all SIFT features are combined together to form a single feature. In order to reduce the identification errors, the Dempster-Shafer decision theory is applied to fuse the two matching techniques. The proposed algorithms are evaluated with the ORL and the IITK face databases. The experimental results demonstrate the effectiveness and potential of the proposed face recognition techniques also in the case of partially occluded faces or with missing information.Comment: 7 pages, 6 figures, IEEE Computer Vision and Pattern Recognition Workshop on Biometric

Approximation of Belief Functions by Minimizing Euclidean Distance

Abstract. This paper addresses the approximation of belief functions by minimizing the Euclidean distance to a given belief function in the set of probability functions. The special case of Dempster-Shafer belief functions is considered in particular detail. It turns out that, in this case, an explicit solution by means of a projective transformation can be given. Furthermore, we also consider more general concepts of belief. We state that the approximation by means of minimizing the Euclidean distance, unlike other methods that are restricted to Dempster-Shafer belief, works as well. However, the projective transformation formula cannot necessarily be applied in these more general settings

A distance measure of interval-valued belief structures

Interval-valued belief structures are generalized from belief function theory, in terms of basic belief assignments from crisp to interval numbers. The distance measure has long been an essential tool in belief function theory, such as conflict evidence combinations, clustering analysis, belief function and approximation. Researchers have paid much attention and proposed many kinds of distance measures. However, few works have addressed distance measures of interval-valued belief structures up. In this paper, we propose a method to measure the distance of interval belief functions. The method is based on an interval-valued one-dimensional Hausdorff distance and Jaccard similarity coefficient. We show and prove its properties of non-negativity, non-degeneracy, symmetry and triangle inequality. Numerical examples illustrate the validity of the proposed distance

An Intelligent Complex Event Processing with D

Efficient matching of incoming mass events to persistent queries is fundamental to complex event processing systems. Event matching based on pattern rule is an important feature of complex event processing engine. However, the intrinsic uncertainty in pattern rules which are predecided by experts increases the difficulties of effective complex event processing. It inevitably involves various types of the intrinsic uncertainty, such as imprecision, fuzziness, and incompleteness, due to the inability of human beings subjective judgment. Nevertheless, D numbers is a new mathematic tool to model uncertainty, since it ignores the condition that elements on the frame must be mutually exclusive. To address the above issues, an intelligent complex event processing method with D numbers under fuzzy environment is proposed based on the Technique for Order Preferences by Similarity to an Ideal Solution (TOPSIS) method. The novel method can fully support decision making in complex event processing systems. Finally, a numerical example is provided to evaluate the efficiency of the proposed method

Multisensor data fusion and belief functions for robust singularity detection in signals

This paper addresses the problem of robust detection of signal singularity in hostile environments using multisensor data fusion. Measurement uncertainty is usually treated in a probabilistic way, assuming lack of knowledge is totally due to random effects. However, this approach fails when other effects, such as sensor failure, are involved. In order to improve the robustness of singularity detection, an evidence theory based approach is proposed for both modeling (data alignment) and merging (data fusion) information coming from multiple redundant sensors. Whereas the fusion step is done classically, the proposed method for data alignment has been designed to improve singularity detection performances in multisensor cases. Several case studies have been designed to suit real life situations. Results provided by both probabilistic and evidential approaches are compared. Evidential methods show better behavior facing sensors dysfunction and the proposed method takes fully advantage of component redundancy

Dempster-Shafer Theory Computation using Constraint Programming

Στην πτυχιακή εργασία αυτή μας απασχολεί το θέμα του Χειρισμού Αβεβαιότητας στον τομέα της Αναπαράστασης Γνώσης, και ειδικότερα χρησιμοποιώντας τη θεωρία των Dempster-Shafer. Σκοπός της μελέτης είναι η αποδοτικότερη χρήση της θεωρίας, χρησιμοποιώντας Προγραμματισμό με Περιορισμούς. Υλοποιούμε μια τέτοια μέθοδο σε ECLiPSe Prolog, και την δοκιμάζουμε και συγκρίνουμε σε σχέση με μια μέθοδο που δεν χρησιμοποιεί Προγραμματισμό με Περιορισμούς, για έναν αριθμό τυχαίων δοκιμών, υπολογίζοντας τη συνενωμένη συνάρτηση ανάθεσης πιθανότητας και την εμπιστοσύνη για ένα τυχαίο σύνολο. Τα αποτελέσματα είναι ενθαρρυντικά, καθώς κατορθώσαμε μείωση του χρόνου εκτέλεσης σε όλες τις περιπτώσεις.In this thesis, we deal with the subject of Handling Uncertainty in the field of Knowledge Representation, and in particular using Dempster-Shafer theory. The purpose of this project is to optimize the computation of the theory, focusing on the burden that Dempster’s rule of combination introduces, by using Constraint Programming. We implement such a method in ECLiPSe Prolog and test and compare it to a method not utilizing Constraint Programming for a number of random generated test cases, computing the mass joint and the belief of a random set. The results are overall encouraging, as in all cases run-time is reduced

Étude des algorithmes d'approximation de fonctions de croyance généralisées

La recherche présentée ici consiste à résoudre le problème de difficulté calculatoire de la fusion d’informations dans le cadre de la théorie de l’évidence de Dempster-Shafer, ainsi que celui de la théorie de Dezert-Smarandache. On présente des études sur l’utilisation d’une variété d’algorithmes d’approximation connus ainsi que sur un nouvel algorithme d’approximation. On présente aussi une étude sur les métriques connues de distance entre corps d’évidence ainsi que deux nouvelles métriques. Enfin, on montre une étude de la possibilité d’employer une méthode d’optimisation afin de sélectionner automatiquement les paramètres d’approximation à l’aide de critères de performance. Mots-clés : Dezert, Smarandache, Dempster, Shafer, Fusion, Fonctions de croyance.This research is about the solving of the computational difficulty of data fusion in the evidence theory of Dempster-Shafer theory and Dezert-Smarandache theory. We study the use of a variety of known approximation algorithms as well as a new approximation algorithm that we propose. We also study known metrics between bodies of evidence as well as two new metrics that we develop. Finally, we study the possibility of using an optimization method to automatically select the parameters of approximation with performance criteria. Keywords: Dezert, Smarandache, Dempster, Shafer, Fusion, Belief functions