Vectorizing Face Images by Interpreting Shape and Texture Computations

The correspondence problem in computer vision is basically a matching task between two or more sets of features. In this paper, we introduce a vectorized image representation, which is a feature-based representation where correspondence has been established with respect to a reference image. This representation has two components: (1) shape, or (x, y) feature locations, and (2) texture, defined as the image grey levels mapped onto the standard reference image. This paper explores an automatic technique for "vectorizing" face images. Our face vectorizer alternates back and forth between computation steps for shape and texture, and a key idea is to structure the two computations so that each one uses the output of the other. A hierarchical coarse-to-fine implementation is discussed, and applications are presented to the problems of facial feature detection and registration of two arbitrary faces

Example Based Image Analysis and Synthesis

Image analysis and graphics synthesis can be achieved with learning techniques using directly image examples without physically-based, 3D models. In our technique: -- the mapping from novel images to a vector of "pose" and "expression" parameters can be learned from a small set of example images using a function approximation technique that we call an analysis network; -- the inverse mapping from input "pose" and "expression" parameters to output images can be synthesized from a small set of example images and used to produce new images using a similar synthesis network. The techniques described here have several applications in computer graphics, special effects, interactive multimedia and very low bandwidth teleconferencing

Face Recognition from One Example View

If we are provided a face database with only one example view per person, is it possible to recognize new views of them under a variety of different poses, especially views rotated in depth from the original example view? We investigate using prior knowledge about faces plus each single example view to generate virtual views of each person, or views of the face as seen from different poses. Prior knowledge of faces is represented in an example-based way, using 2D views of a prototype face seen rotating in depth. The synthesized virtual views are evaluated as example views in a view-based approach to pose-invariant face recognition. They are shown to improve the recognition rate over the scenario where only the single real view is used

Face Recognition Under Varying Pose

While researchers in computer vision and pattern recognition have worked on automatic techniques for recognizing faces for the last 20 years, most systems specialize on frontal views of the face. We present a face recognizer that works under varying pose, the difficult part of which is to handle face rotations in depth. Building on successful template-based systems, our basic approach is to represent faces with templates from multiple model views that cover different poses from the viewing sphere. Our system has achieved a recognition rate of 98% on a data base of 62 people containing 10 testing and 15 modelling views per person

“We do investigate ourselves”: figurative assessment practices as meaning-making in English education

In this study of microteaching in a secondary English methods course, we intentionally stray from normative assessment practice, instead asking pre-service teachers to provide feedback on their peers’ microteaching using assessment practices designed to orient them figuratively. The term ‘figurative’ refers to ‘figurative language’: the bringing together of multiple, seemingly unrelated things, through associative configurations, and placing them side-by-side in order to reorient thought towards new or unexpected meanings. This study reframes assessment, not as a means of collecting data on what students have learned from a given lesson in order to evaluate and augment learning, but instead figuratively, as providing opportunities to expand and imagine ways of meaning-making through and with assessment. We examine in detail four modes of figurative assessment practices through which we sought to surprise and disorient students, producing new and different kinds of responses to microteaching that went beyond normative feedback practices

I never quite got it, what they meant: an introduction to poetic teaching

We have become well-familiar with how unpoetic teaching can be. The prevalence, furthered by much recent reform, of a systematic school culture focused on accountability, standardisation, and learnification often renders teaching dehumanised work. This paper theorises a poetics of teaching. We begin considering poetics, focusing on figurative language as a concept at the core of the art. Figurative language offers a model for figurative education, in which teachers treat their practice as metaphors treat language, a move that opens education towards complexity and ambiguity. Further, we consider what makes poetry matter to people: resonance, or the relational aspects of writing. We explore resonance in conversation with philosophies of relationality, theorising how poetic teaching necessitates an engagement with the relational. We find what may be required to teach poetically is risk-taking, risks all the more beautiful for the ways they engage teachers and students as complex persons doing meaningful work

Arguments Against a Configural Processing Account of Familiar Face Recognition

Face recognition is a remarkable human ability, which underlies a great deal of people's social behavior. Individuals can recognize family members, friends, and acquaintances over a very large range of conditions, and yet the processes by which they do this remain poorly understood, despite decades of research. Although a detailed understanding remains elusive, face recognition is widely thought to rely on configural processing, specifically an analysis of spatial relations between facial features (so-called second-order configurations). In this article, we challenge this traditional view, raising four problems: (1) configural theories are underspecified; (2) large configural changes leave recognition unharmed; (3) recognition is harmed by nonconfigural changes; and (4) in separate analyses of face shape and face texture, identification tends to be dominated by texture. We review evidence from a variety of sources and suggest that failure to acknowledge the impact of familiarity on facial representations may have led to an overgeneralization of the configural account. We argue instead that second-order configural information is remarkably unimportant for familiar face recognition

Identity From Variation : Representations of Faces Derived From Multiple Instances

Research in face recognition has tended to focus on discriminating between individuals, or "telling people apart." It has recently become clear that it is also necessary to understand how images of the same person can vary, or "telling people together." Learning a new face, and tracking its representation as it changes from unfamiliar to familiar, involves an abstraction of the variability in different images of that person's face. Here, we present an application of principal components analysis computed across different photos of the same person. We demonstrate that people vary in systematic ways, and that this variability is idiosyncratic-the dimensions of variability in one face do not generalize well to another. Learning a new face therefore entails learning how that face varies. We present evidence for this proposal and suggest that it provides an explanation for various effects in face recognition. We conclude by making a number of testable predictions derived from this framework