Kinship Verification from Videos using Spatio-Temporal Texture Features and Deep Learning

Automatic kinship verification using facial images is a relatively new and challenging research problem in computer vision. It consists in automatically predicting whether two persons have a biological kin relation by examining their facial attributes. While most of the existing works extract shallow handcrafted features from still face images, we approach this problem from spatio-temporal point of view and explore the use of both shallow texture features and deep features for characterizing faces. Promising results, especially those of deep features, are obtained on the benchmark UvA-NEMO Smile database. Our extensive experiments also show the superiority of using videos over still images, hence pointing out the important role of facial dynamics in kinship verification. Furthermore, the fusion of the two types of features (i.e. shallow spatio-temporal texture features and deep features) shows significant performance improvements compared to state-of-the-art methods.Comment: 7 page

Modélisation géométrique par contraintes : quelques méthodes de résolution

Diverses techniques de modélisation sont utilisées en synthèse d'images et en CAO (conception assistée par ordinateur) pour produire des images réalistes et analyser les propriétés géométriques des objets solides modélisés. Cependant, malgré les progrès récents, la conception de formes géométriques reste une tâche complexe. Les objets géométriques que veut modéliser l'utilisateur doivent vérifier certaines propriétés, traditionnellement appelées contraintes. Pour pallier ces inconvénients certains systèmes de modélisation fournissent des outils de spécification des formes par des contraintes géométriques. Nous proposons dans cette thèse deux méthodes de résolution du système de contraintes. La première méthode étudie les graphes bipartis sous-jacents aux systèmes d'équations. Nous montrons qu'il est possible de décomposer polynomialement ces systèmes en sous-systèmes sur-contraints (plus d'équations que d'inconnues), sous-contraints (plus d'inconnues que d'équations) et bien-contraints (autant d'équations que d'inconnues) a partir du graphe biparti. La deuxième méthode proposée étudie les différentes configurations induites par des contraintes de distances, d'angles et de tangences entre points, droites et cercles. Les entités géométriques sont déterminées par un algorithme de réduction de graphes et un système à base de règles.No abstrac

Some Irreducible Laman Graphs

In graph theory, Laman graphs [1] describe the minimally rigid plane structures composed of bars and joints. Removing a bar leads to the non-rigidity of the structure. In the domain of geometric constraints solving, this property is commonly referred to as well-constrained graphs or structures. Formally, a graph G=(V, E) where |V|=n and |E|=m is a Laman graph if and only if m=2*n-3 and m’ 2*n’-3 for any induced sub-graph G'=(V',E ') where |V'|=n' and |E'|=m'. Another characterization of rigid structures was given in 1911 by Henneberg [2] for which any minimal rigid plane structure is obtained starting from an edge joining two vertices and adding one vertex at a time using one of the two following operations. Operation HI: add a new vertex v to G, then connect v to two chosen vertices u and w from G via two new edges (v, u) and (v, w). Operation HII: add a new vertex v to G, chose an edge (u, w) and a vertex z to G, then add three edges (v, u), (v, w) and (v, z) to G, finally delete the edge (u, w).An irreducible Laman graph is a graph G=(V, E) where |V|=n, |E|=m , m=2*n-3 and m’<2*n’-3 for any induced sub-graph G'=(V',E ') where |V'| = n' and |E'|=m'. An irreducible Laman graph is a graph that contains no Laman subgraphs. Based on the constraint system reduction algorithm [3], Laman irreducible graphs are obtained using the generator of 2D Geometric Constraint Graphs described in [4]. Laman irreducible graphs with up to 100 vertices are given in the appendix

Reduction Of Constraint Systems

Geometric modeling by constraints leads to large systems of algebraic equations. This paper studies bipartite graphs underlaid by systems of equations. It shows how these graphs make possible to polynomially decompose these systems into well constrained, over-, and underconstrained subsystems. This paper also gives an efficient method to decompose well constrained systems into irreducible ones. These decompositions greatly speed up the resolution in case of reducible systems. They also allow debugging systems of constraints. Key Words: geometric modeling, constraints, bipartite graphs, matching, maximum matching, perfect matching. 1. INTRODUCTION Geometric modeling by constraints is an interesting approach in CAD. Typically, in 2D, geometric modeling by constraints specifies geometrical objects such as points, lines, circles, conics by a set of constraints : distances between points, points and lines, parallel lines, angles between lines, incidence relations between points and lines,..

Sur la réductibilité des graphes de contraintes géométriques

La modélisation géométrique par contraintes dont les applications intéressent des communautés issues de divers domaines tels l'ingénierie mécanique, la conception assistée par ordinateur, le calcul symbolique ou la chimie moléculaire est maintenant intégré dans les outils standards de modélisation. Dans cette discipline une forme géométrique est spécifiée par les relations que doivent vérifier les composants de cette forme au lieu de spécifier explicitement ces composants. Le but de la résolution est de déduire la forme répondant à toutes ces contraintes. Diverses méthodes ont été proposées pour résoudre ce problème. Nous nous intéresserons spécifiquement aux méthodes dites graphiques ou basées-graphes avec application à l'espace bidimensionnel

Biohashing for securing fingerprint minutiae templates

International audienceThe storage of fingerprints is an important issue as this biometric modality is more and more deployed for real applications. The a prori impossibility to revoke a biometric template (like a password) in case of theft, is a major concern for privacy reasons. We propose in this paper a new method to secure fingerprint minutiae templates by storing a biocode while keeping good recognition results. We show the efficiency of the method in comparison to some published methods for different scenarios

Medical Image Segmentation Using Hidden Markov Random Field A Distributed Approach

Abstract. Medical imaging applications produce large sets of similar images. The huge amount of data makes the manual analysis and interpretation a fastidious task. Medical image segmentation is thus an important process in image processing used to partition the images into different regions (e.g. gray matter, white matter and cerebrospinal fluid). Hidden Markov Random Field (HMRF) Model and Gibbs distributions provide powerful tools for image modeling. In this paper, we use a HMRF model to perform segmentation of volumetric medical images. We have a problem with incomplete data. We seek the segmented images according to the MAP (Maximum A Posteriori) criterion. MAP estimation leads to the minimization of an energy function. This problem is computationally intractable. Therefore, optimizations techniques are used to compute a solution. We will evaluate the segmentation upon two major factors: the time of calculation and the quality of segmentation. Processing time is reduced by distributing the computation of segmentation on a powerful and inexpensive architecture that consists of a cluster of personal computers. Parallel programming was done by using the standard MPI (Message Passing Interface)