Job face à son Dieu : « Le châtiment de Dieu était ma terreur » (Jb 31,23)

Conférence présentée à la Faculté de théologie et de sciences des religions de l’Université de Montréal le 11 septembre 2013. © Jean Duhaime

De l'engagement communautaire à la défense du territoire : les voix politiques des femmes innues face à l'exploitation minière

Le point de départ de cette étude est un sujet d’actualité qui fait l’objet de controverses au Québec depuis 2011 : le Plan Nord, un projet de développement économique visant la mise en valeur et l’exploitation des ressources naturelles au nord du Québec. En particulier, cette étude s’intéresse à la résistance des innu ishkueu (femmes innues) à ce projet, plus précisément dans un contexte d’exploitation minière. L’angle choisi est celui du parcours d’engagement des actrices participant à ces mouvements de résistance. L’analyse proposée s’appuie sur une enquête de terrain de trois mois, réalisée au sein des communautés de Uashat mak Mani- Utenam et Matimekush-Lac John, au cours de laquelle des entretiens semi-dirigés furent réalisés. Conjuguant les théories féministes autochtones, la notion de résistance au quotidien et l’étude des carrières militantes, cette recherche a pour objectif de démystifier certaines dimensions des voix politiques féminines innues dans la défense du territoire. Dans un premier temps, elle présente une perspective ethnohistorique de la résistance des femmes innues face à l’exploitation minière. Le but est de contribuer aux initiatives offrant une alternative à la version dominante de l’histoire minière du Québec (blanche, masculine), qui a doublement occulté les savoirs situés des femmes autochtones. Dans un second temps, elle s’attarde aux parcours d’engagement des femmes rencontrées et à leur engagement communautaire. Ceci nous a amené à examiner dans quelle mesure les modes d’action locale des femmes innues au sein de leur communauté se transfèrent dans la défense du territoire. Enfin, elle s’intéresse à la construction des subjectivités politiques des innu ishkueu en s’appuyant sur des repères théoriques situant la politique de résistance des femmes autochtones.

L’approche des capabilités de Martha Nussbaum face aux enjeux multiculturels des sociétés libérales occidentales

Se situant au confluent du libéralisme politique rawlsien et de l’anthropologie néoaristotélicienne, l’approche des capabilités de Martha Nussbaum offre un cadre théorique permettant de répondre aux tensions multiculturelles. Cet article constitue une analyse détaillée de la réponse de Nussbaum à ces enjeux, qui prétend unir un pluralisme axiologique à un universalisme moral fort. Nous avancerons que la démarche entreprise par la philosophe porte une tension entre le libéralisme politique rawlsien et le cadre conceptuel apporté par la liste des capabilités. Cette liste pose une difficulté par le déficit démocratique de ses fondements, n’intégrant pas suffisamment des normes d’inclusion et de représentation. Dans un dernier temps, nous tenterons de pallier ces problèmes en intégrant à l’approche de Nussbaum des exigences délibératives.

A Prototype for a Multimodal Biometric Security system based on Face and Audio Signatures

Any automatically measurable, robust and distinctive physical characteristic or personal trait that can be used to identify an individual or verify the claimed identity of an individual, referred to as biometrics, has gained significant interest in the wake of heightened concerns about security and rapid advancements in networking, communication and mobility. Multimodal biometrics is expected to be ultra-secure and reliable, due to the presence of multiple and independent—verification clues. In this study, a multimodal biometric system utilising audio and facial signatures has been implemented and error analysis has been carried out. A total of one thousand face images and 250 sound tracks of 50 users are used for training the proposed system. To account for the attempts of the unregistered signatures data of 25 new users are tested. The short term spectral features were extracted from the sound data and Vector Quantization was done using K-means algorithm. Face images are identified based on Eigen face approach using Principal Component Analysis. The success rate of multimodal system using speech and face is higher when compared to individual unimodal recognition systems

Face Recognition using Probabilistic Neural Networks

In this paper we address the problem of face detection and recognition of grey scale frontal view images. We propose a face recognition system based on probabilistic neural networks (PNN) architecture. The system is implemented using voronoi/ delaunay tessellations and template matching. Images are segmented successfully into homogeneous regions by virtue of voronoi diagram properties. Face verification is achieved using matching scores computed by correlating edge gradients of reference images. The advantage of classification using PNN models is its short training time. The correlation based template matching guarantees good classification results

Face Recognition using Probabilistic Neural Networks

n this paper we address the problem of face detection and recognition of grey scale frontal view images. We propose a face recognition system based on probabilistic neural networks (PNN) architecture. The system is implemented using voronoi/ delaunay tessellations and template matching. Images are segmented successfully into homogeneous regions by virtue of voronoi diagram properties. Face verification is achieved using matching scores computed by correlating edge gradients of reference images. The advantage of classification using PNN models is its short training time. The correlation based template matching guarantees good classification results.

Individuals' (re-)action in the face of global warming

Climate change remains a major challenge for today’s and future societies due to its immense impacts on human lives and the natural environment. This thesis investigates the extent to which individuals are willing and prepared to voluntarily contribute to climate protection and to adjust to new climatic conditions in order to cope with the consequences of climate change and reduce the severity of potential negative impacts. The thesis thereby combines research in the fields of the private provision of environmental public goods and adaptation to climate change, which is still widely unconnected in the existing literature. The six contributions of this thesis mainly focus on microeconometric analyses using data from international surveys in China, Germany, and the USA. The main findings are: (i) A substantial share of individuals is willing to voluntarily contribute to climate protection and to adapt to climatic change. The engagement in both strategies is positively interrelated at the individual level and the analyses reveal hardly any evidence that adaptation activities crowd out individuals’ incentives to engage in climate protection. (ii) The main determinants of individuals’ adaptation activities seem to be the subjective risk perception as well as socio-economic and socio-demographic characteristics like age, gender, education, and income, while their climate protection efforts are found to be broadly motivated by financial advantages from these activities and additional immaterial benefits. (iii) The empirical findings also suggest a significantly positive relationship between certain climate protection activities. Substitutions are found to occur merely if one measure is perceived to be more effective in providing climate protection or if individuals have high environmental preferences. (iv) This thesis further reveals a common understanding of a (normatively) fair burden-sharing in international climate policy across citizens in China, Germany, and the USA. The highest preferences are found for the accountability principle.

Rotation Invariant Real-time Face Detection and Recognition System

In this report, a face recognition system that is capable of detecting and recognizing frontal and rotated faces was developed. Two face recognition methods focusing on the aspect of pose invariance are presented and evaluated - the whole face approach and the component-based approach. The main challenge of this project is to develop a system that is able to identify faces under different viewing angles in realtime. The development of such a system will enhance the capability and robustness of current face recognition technology. The whole-face approach recognizes faces by classifying a single feature vector consisting of the gray values of the whole face image. The component-based approach first locates the facial components and extracts them. These components are normalized and combined into a single feature vector for classification. The Support Vector Machine (SVM) is used as the classifier for both approaches. Extensive tests with respect to the robustness against pose changes are performed on a database that includes faces rotated up to about 40 degrees in depth. The component-based approach clearly outperforms the whole-face approach on all tests. Although this approach isproven to be more reliable, it is still too slow for real-time applications. That is the reason why a real-time face recognition system using the whole-face approach is implemented to recognize people in color video sequences.

Example Based Learning for View-Based Human Face Detection

We present an example-based learning approach for locating vertical frontal views of human faces in complex scenes. The technique models the distribution of human face patterns by means of a few view-based "face'' and "non-face'' prototype clusters. At each image location, the local pattern is matched against the distribution-based model, and a trained classifier determines, based on the local difference measurements, whether or not a human face exists at the current image location. We provide an analysis that helps identify the critical components of our system.

Conditions for Viewpoint Dependent Face Recognition

Poggio and Vetter (1992) showed that learning one view of a bilaterally symmetric object could be sufficient for its recognition, if this view allows the computation of a symmetric, "virtual," view. Faces are roughly bilaterally symmetric objects. Learning a side-view--which always has a symmetric view--should allow for better generalization performances than learning the frontal view. Two psychophysical experiments tested these predictions. Stimuli were views of shaded 3D models of laser-scanned faces. The first experiment tested whether a particular view of a face was canonical. The second experiment tested which single views of a face give rise to best generalization performances. The results were compatible with the symmetry hypothesis: Learning a side view allowed better generalization performances than learning the frontal view.

An Empirical Comparison of SNoW and SVMs for Face Detection

Impressive claims have been made for the performance of the SNoW algorithm on face detection tasks by Yang et. al. [7]. In particular, by looking at both their results and those of Heisele et. al. [3], one could infer that the SNoW system performed substantially better than an SVM-based system, even when the SVM used a polynomial kernel and the SNoW system used a particularly simplistic 'primitive' linear representation. We evaluated the two approaches in a controlled experiment, looking directly at performance on a simple, fixed-sized test set, isolating out 'infrastructure' issues related to detecting faces at various scales in large images. We found that SNoW performed about as well as linear SVMs, and substantially worse than polynomial SVMs.

Face Detection in Still Gray Images

We present a trainable system for detecting frontal and near-frontal views of faces in still gray images using Support Vector Machines (SVMs). We first consider the problem of detecting the whole face pattern by a single SVM classifer. In this context we compare different types of image features, present and evaluate a new method for reducing the number of features and discuss practical issues concerning the parameterization of SVMs and the selection of training data. The second part of the paper describes a component-based method for face detection consisting of a two-level hierarchy of SVM classifers. On the first level, component classifers independently detect components of a face, such as the eyes, the nose, and the mouth. On the second level, a single classifer checks if the geometrical configuration of the detected components in the image matches a geometrical model of a face.

Feature Selection for Face Detection

We present a new method to select features for a face detection system using Support Vector Machines (SVMs). In the first step we reduce the dimensionality of the input space by projecting the data into a subset of eigenvectors. The dimension of the subset is determined by a classification criterion based on minimizing a bound on the expected error probability of an SVM. In the second step we select features from the SVM feature space by removing those that have low contributions to the decision function of the SVM.

Role of color in face recognition

One of the key challenges in face perception lies in determining the contribution of different cues to face identification. In this study, we focus on the role of color cues. Although color appears to be a salient attribute of faces, past research has suggested that it confers little recognition advantage for identifying people. Here we report experimental results suggesting that color cues do play a role in face recognition and their contribution becomes evident when shape cues are degraded. Under such conditions, recognition performance with color images is significantly better than that with grayscale images. Our experimental results also indicate that the contribution of color may lie not so much in providing diagnostic cues to identity as in aiding low-level image-analysis processes such as segmentation.