A manifold approach to face recognition from low quality video across illumination and pose using implicit super-resolution

We consider the problem of matching a face in a low resolution query video sequence against a set of higher quality gallery sequences. This problem is of interest in many applications, such as law enforcement. Our main contribution is an extension of the recently proposed Generic Shape-Illumination Manifold (gSIM) framework. Specifically, (i) we show how super-resolution across pose and scale can be achieved implicitly, by off-line learning of subsampling artefacts; (ii) we use this result to propose an extension to the statistical model of the gSIM by compounding it with a hierarchy of subsampling models at multiple scales; and (iii) we describe an extensive empirical evaluation of the method on over 1300 video sequences – we first measure the degradation in performance of the original gSIM algorithm as query sequence resolution is decreased and then show that the proposed extension produces an error reduction in the mean recognition rate of over 50%.

Super Blooper? Time to halt the superannuation juggernaut?

The superannuation industry has increased almost exponentially in Australia over the past decade. The main reason for this is because government  regulation compels employers to pay a fixed portion of employees’ salary  towards superannuation. In this article we suggest that the unremitting  government policy of coercing money towards superannuation is flawed. Superannuation is wrong at two levels. First, on an economic analysis, the evidence does not suggest that (i) individuals who invest in superannuation are necessarily better off than those who apply their income elsewhere; and (ii) there is no evidence that absent a coercive superannuation scheme the government will be unable to sustain people into their old age. Second, at the human and societal level, studies of human well-being show that coercing people to make spending decisions is inimical to human happiness. People flourish best when they are in control of their activities, including their finances. Left to their own devices, many people will not save for a rainy day; however, on balance it is probably better off to be a bit poorer in retirement than to have been deprived of the opportunity to spend 9% of one’s income over the period of one’s working life – when one’s needs are the greatest. Compulsory superannuation should be abolished. Money currently paid as a compulsory superannuation contribution should instead be paid to the employee as a salary.

Multi-wavelets from B-spline super-functions with approximation order

Approximation order is an important feature of all wavelets. It implies that polynomials up to degree p−1 are in the space spanned by the scaling function(s). In the scalar case, the scalar sum rules determine the approximation order or the left eigenvectors of the infinite down-sampled convolution matrix H determine the combinations of scaling functions required to produce the desired polynomial. For multi-wavelets the condition for approximation order is similar to the conditions in the scalar case. Generalized left eigenvectors of the matrix H_f; a finite portion of H determines the combinations of scaling functions that produce the desired superfunction from which polynomials of desired degree can be reproduced. The superfunctions in this work are taken to be B-splines. However, any refinable function can serve as the superfunction. The condition of approximation order is derived and new, symmetric, compactly supported and orthogonal multi-wavelets with approximation orders one, two, three and four are constructed.

Super water repellent fabrics produced by silica nanoparticle-containing coating

In this paper, we report on superhydrophobic fabrics (polyester, wool and cotton) produced by a wet-chemical coating technique. The coating solutions were synthesized by the co-hydrolysis of two silane precursors, tetraethyl orthosilicate (TEOS) and an alkylsilane, in an alkaline condition. Without any purification, the as-hydrolyzed solutions were directly used to treat fabrics, and the treated fabrics had water contact angles (CA) as high as 170º and sliding angles (SA) as low as 5º. Three alkylsilanes have been used for the synthesis of the coating solutions, and all contain three hydrolysable alkoxyl groups and one non-hydrolysable alkyl, but with different chain lengths (C1, C8 and C16). It was found that the CA value increased with an increase in the alkyl chain length, while the SA showed a reverse trend. When the functional group had a C16 alkyl, the treated fabric surfaces were highly superhydrophobic, with the CA not being affected much by the fabric type, while the SA values were slightly affected by the original wettability of the fabric substrates. The superhydrophobic feature was attributed to a highly rough surface formed by the particulate coating. Aside from the superhydrophobicity, the influence of the coating on the fabric softness was also examined.

Making difference SUPER : a critical approach to the politics of celebrity-authored children's books

Recent years have seen a flood of celebrity-authored children’s books enter the children’s literature marketplace. This paper tracks the evolution of this phenomenon as it pertains to contemporary celebrity culture, and responds to the question: what are the embedded messages about identity at play in such texts? The key contention here is that these works endorse celebrity as the most desirable identity category for the children of the new millennium, going so far as to position difference as a commodity to be traded in the quest for fame. Using critical theory around celebrity culture, and through a close reading of a short celebrity-authored picture book, this paper demonstrates that far from being innocuous pieces of popular culture, such books beckon children into a world that increasingly values visibility and stardom above all else.

Super-realistic rendering using real-time tweening

The realism of contemporary computer graphics (and especially Virtual Reality {VR}) is limited by the great computational cost of rendering objects of appropriate complexity with convincing lighting and surface effects. We introduce a framework that allows rendering of objects in true photographic quality using tweening. The simple but effective design of our system allows us not only to perform the necessary operations in real-time on standard hardware, but also achieve other effects like morphing. Furthermore, it is shown how our system can be gainfully employed in non-VR contexts like extreme low-bandwidth video-conferencing and others.

Robust learning of discriminative projection for multicategory classification on the Stiefel manifold

Learning a robust projection with a small number of training samples is still a challenging problem in face recognition, especially when the unseen faces have extreme variation in pose, illumination, and facial expression. To address this problem, we propose a framework formulated under statistical learning theory that facilitates robust learning of a discriminative projection. Dimensionality reduction using the projection matrix is combined with a linear classifier in the regularized framework of lasso regression. The projection matrix in conjunction with the classifier parameters are then found by solving an optimization problem over the Stiefel manifold. The experimental results on standard face databases suggest that the proposed method outperforms some recent regularized techniques when the number of training samples is small.

Supervised subspace learning with multi-class Lagrangian SVM on the Grassmann manifold

Learning robust subspaces to maximize class discrimination is challenging, and most current works consider a weak connection between dimensionality reduction and classifier design. We propose an alternate framework wherein these two steps are combined in a joint formulation to exploit the direct connection between dimensionality reduction and classification. Specifically, we learn an optimal subspace on the Grassmann manifold jointly minimizing the classification error of an SVM classifier. We minimize the regularized empirical risk over both the hypothesis space of functions that underlies this new generalized multi-class Lagrangian SVM and the Grassmann manifold such that a linear projection is to be found. We propose an iterative algorithm to meet the dual goal of optimizing both the classifier and projection. Extensive numerical studies on challenging datasets show robust performance of the proposed scheme over other alternatives in contexts wherein limited training data is used, verifying the advantage of the joint formulation.

Learning to think, learning to learn : super teaching for super success

The early childhood years are a busy, exciting time. New discoveries, skills and competencies are a regular part of life for a young child. Early childhood teachers have the opportunity to optimise these amazing and important years. In this paper, I will discuss teaching strategies that can turn children’s possibilities into realities. The practices that will be discussed involve expanding thinking, problem-solving and developing hypotheses. These teaching strategies can build on children’s learning dispositions and their strengths and interests to put the ‘wow factor’ into learning.