Commentary on Hinton v. Donaldson (1773)

The Scottish Court of Session, drawing upon principles of the civil law tradition, as well as arguments concerning broader national, social and cultural interests, reject the concept of copyright at common law - a decision that is in direct conflict with that of Millar v. Taylor (1769). Lord Monboddo provides the dissenting opinion, drawing upon the labour theory of property rights, and argues for a unified approach to the issue in relation to the common law of both England and Scotland.
Drawing upon Scottish Records Office archives the commentary explores the background to, and substance of, the decision. It suggests that, given the nature of the economic threat which the Scottish reprint industry posed to the London book trade, particularly in relation to an increasingly lucrative export market, Hinton undermined much of the value of the decision in Millar. The conflict between Millar and Hinton made it almost inevitable that the question of literary property would soon reach the House of Lords.

Face recognition using a unified 3D morphable model

We address the problem of 3D-assisted 2D face recognition in scenarios when the input image is subject to degradations or exhibits intra-personal variations not captured by the 3D model. The proposed solution involves a novel approach to learn a subspace spanned by perturbations caused by the missing modes of variation and image degradations, using 3D face data reconstructed from 2D images rather than 3D capture. This is accomplished by modelling the difference in the texture map of the 3D aligned input and reference images. A training set of these texture maps then defines a perturbation space which can be represented using PCA bases. Assuming that the image perturbation subspace is orthogonal to the 3D face model space, then these additive components can be recovered from an unseen input image, resulting in an improved fit of the 3D face model. The linearity of the model leads to efficient fitting. Experiments show that our method achieves very competitive face recognition performance on Multi-PIE and AR databases. We also present baseline face recognition results on a new data set exhibiting combined pose and illumination variations as well as occlusion.