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In the paper the problem of designing a pattern recognition system for processing incomplete pattern vectors is considered. An efficient method of integrating the small core probability density function (p.d.f.) estimator employing Gaussian kernels with general parameter matrices has been proposed. As a result these general kernels satisfy the basic requirements of integrability and, therefore, they can be used in p.d.f. estimators for classification systems processing incomplete pattern vectors. In comparison with the Gaussian kernel having a diagonal parameter matrix, the general kernel is better suited for reconstructing multivariate p.d.f. of pattern vectors with correlated components. Also determination of the optimal parameters of the general kernel is much simpler for the use of the computationally demanding maximum likelihood parameter estimation method can be obviated.

All the modified Nearest Neighbour methods of pattern classification2–6 developed to reduce the amount of computer storage and time needed for the implementation of a NN classifier require prohibitively costly data preprocessing which involves detailed examination of the neighbouring points to the elements of the reference set. In this paper a method for determining k‐nearest neighbours to a given point is described. The method uses the computationally efficient city block distance to select candidate points for the set of k‐nearest neighbours. In this way the preprocessing time is considerably reduced.

We have long been obsessed with the dream of creating intelligent machines. This vision can be traced back to Greek civilization, and the notion that mortals somehow can create machines that think has persisted throughout history. Until this decade these illusions have borne no substance. The birth of the computer in the 1940s did cause a resurgence of the cybernaut idea, but the computer's role was primarily one of number‐crunching and realists soon came to respect the enormous difficulties in crafting machines that could accomplish even the simplest of human tasks.

The figure of the “Kafkaesque” in law serves often as a stand-in for something like “perverted justice” and ranks prominently among the legal profession as a whole. But we should not soothe ourselves with such obvious clichés surrounding the “Kafkaesque,” rather we must continue to pursue the disturbing challenge Kafka poses for the analysis of the law. It is clear that Kafka’s texts hit a certain nerve of modern law that reaches well beyond these familiar punchlines. It is the task of this article to uncover some of the reasons why Kafka strikes such a strong cord with both legal scholars and people outside of academia alike.

Personal mobile devices currently have access to a significant portion of their user's private sensitive data and are increasingly used for processing mobile payments. Consequently, securing access to these mobile devices is a requirement for securing access to the sensitive data and potentially costly services. The authors propose and evaluate a first version of a pan shot face unlock method: a mobile device unlock mechanism using all information available from a 180° pan shot of the device around the user's head – utilizing biometric face information as well as sensor data of built‐in sensors of the device. The paper aims to discuss these issues.

This approach uses grayscale 2D images, on which the authors perform frontal and profile face detection. For face recognition, the authors evaluate different support vector machines and neural networks. To reproducibly evaluate this pan shot face unlock toolchain, the authors assembled the 2013 Hagenberg stereo vision pan shot face database, which the authors describe in detail in this article.

Current results indicate that the approach to face recognition is sufficient for further usage in this research. However, face detection is still error prone for the mobile use case, which consequently decreases the face recognition performance as well.

The contributions of this paper include: introducing pan shot face unlock as an approach to increase security and usability during mobile device authentication; introducing the 2013 Hagenberg stereo vision pan shot face database; evaluating this current pan shot face unlock toolchain using the newly created face database.