Hybrid 2D and 3D face verification

Face verification is a challenging pattern recognition problem. The face is a biometric that, we as humans, know can be recognised. However, the face is highly deformable and its appearance alters significantly when the pose, illumination or expression changes. These changes in appearance are most notable for texture images, or two-dimensional (2D) data. But the underlying structure of the face, or three dimensional (3D) data, is not changed by pose or illumination variations. Over the past five years methods have been investigated to combine 2D and 3D face data to improve the accuracy and robustness of face verification. Much of this research has examined the fusion of a 2D verification system and a 3D verification system, known as multi-modal classifier score fusion. These verification systems usually compare two feature vectors (two image representations), a and b, using distance or angular-based similarity measures. However, this does not provide the most complete description of the features being compared as the distances describe at best the covariance of the data, or the second order statistics (for instance Mahalanobis based measures). A more complete description would be obtained by describing the distribution of the feature vectors. However, feature distribution modelling is rarely applied to face verification because a large number of observations is required to train the models. This amount of data is usually unavailable and so this research examines two methods for overcoming this data limitation: 1. the use of holistic difference vectors of the face, and 2. by dividing the 3D face into Free-Parts. The permutations of the holistic difference vectors is formed so that more observations are obtained from a set of holistic features. On the other hand, by dividing the face into parts and considering each part separately many observations are obtained from each face image; this approach is referred to as the Free-Parts approach. The extra observations from both these techniques are used to perform holistic feature distribution modelling and Free-Parts feature distribution modelling respectively. It is shown that the feature distribution modelling of these features leads to an improved 3D face verification system and an effective 2D face verification system. Using these two feature distribution techniques classifier score fusion is then examined. This thesis also examines methods for performing classifier fusion score fusion. Classifier score fusion attempts to combine complementary information from multiple classifiers. This complementary information can be obtained in two ways: by using different algorithms (multi-algorithm fusion) to represent the same face data for instance the 2D face data or by capturing the face data with different sensors (multimodal fusion) for instance capturing 2D and 3D face data. Multi-algorithm fusion is approached as combining verification systems that use holistic features and local features (Free-Parts) and multi-modal fusion examines the combination of 2D and 3D face data using all of the investigated techniques. The results of the fusion experiments show that multi-modal fusion leads to a consistent improvement in performance. This is attributed to the fact that the data being fused is collected by two different sensors, a camera and a laser scanner. In deriving the multi-algorithm and multi-modal algorithms a consistent framework for fusion was developed. The consistent fusion framework, developed from the multi-algorithm and multimodal experiments, is used to combine multiple algorithms across multiple modalities. This fusion method, referred to as hybrid fusion, is shown to provide improved performance over either fusion system on its own. The experiments show that the final hybrid face verification system reduces the False Rejection Rate from 8:59% for the best 2D verification system and 4:48% for the best 3D verification system to 0:59% for the hybrid verification system; at a False Acceptance Rate of 0:1%

Annotation Graphs and Servers and Multi-Modal Resources: Infrastructure for Interdisciplinary Education, Research and Development

Annotation graphs and annotation servers offer infrastructure to support the analysis of human language resources in the form of time-series data such as text, audio and video. This paper outlines areas of common need among empirical linguists and computational linguists. After reviewing examples of data and tools used or under development for each of several areas, it proposes a common framework for future tool development, data annotation and resource sharing based upon annotation graphs and servers.Comment: 8 pages, 6 figure

Seeking the real : the special case of Peter Zumthor

Peter Zumthor writes about "developing an architecture which sets out from and returns to real things" referring to both his own design process and the qualities he wishes his architecture to convey. In an architecture culture long accustomed to media saturation and the image, the phrase 'real things' is provocative and potentially archaic. This paper examines what Zumthor means by that term by investigating how he establishes the core ideas or principles that come to inform design development; namely, by his approach to a brief, a site, and a context. The paper draws on his writings as well as our own experience of being in his buildings, particularly through a rare interview that we conducted with him in his new house and atelier in Haldenstein

Innovative psychological treatments for depression

A number of high-intensity psychosocial interventions have been shown to be as efficacious as and more enduring than medications in the treatment of nonpsychotic depression. Moreover, there have been important advances in the development of strategies to facilitate the selection of the best treatment for a given patient with a depression diagnosis. However, the demand for services is too great to be met by conventional high-intensity approaches alone. Some of the most exciting work in recent years has focused on the development of low-intensity approaches that can benefit many people and do so cost-effectively

Assessing Devolution in the Canadian North: A Case Study of the Yukon Territory

Despite a rich literature on the political and constitutional development of the Canadian territorial North, few scholars have examined the post-devolution environment in Yukon. This lacuna is surprising since devolution is frequently cited as being crucial to the well-being of Northerners, leading both the Government of Nunavut and the Government of the Northwest Territories to lobby the federal government to devolve lands and resources to them. This paper provides an updated historical account of devolution in Yukon and assesses its impact on the territory since 2003. Relying mainly on written resources and 16 interviews with Aboriginal, government, and industry officials in the territory, it highlights some broad effects of devolution and specifically analyzes the processes of obtaining permits for land use and mining. Our findings suggest that devolution has generally had a positive effect on the territory, and in particular has led to more efficient and responsive land use and mining permit processes