Face Recognition using Segmental Euclidean Distance

In this paper an attempt has been made to detect the face using the combination of integral image along with the cascade structured classifier which is built using Adaboost learning algorithm. The detected faces are then passed through a filtering process for discarding the non face regions. They are individually split up into five segments consisting of forehead, eyes, nose, mouth and chin. Each segment is considered as a separate image and Eigenface also called principal component analysis (PCA) features of each segment is computed. The faces having a slight pose are also aligned for proper segmentation. The test image is also segmented similarly and its PCA features are found. The segmental Euclidean distance classifier is used for matching the test image with the stored one. The success rate comes out to be 88 per cent on the CG(full) database created from the databases of California Institute and Georgia Institute. However the performance of this approach on ORL(full) database with the same features is only 70 per cent. For the sake of comparison, DCT(full) and fuzzy features are tried on CG and ORL databases but using a well known classifier, support vector machine (SVM). Results of recognition rate with DCT features on SVM classifier are increased by 3 per cent over those due to PCA features and Euclidean distance classifier on the CG database. The results of recognition are improved to 96 per cent with fuzzy features on ORL database with SVM.Defence Science Journal, 2011, 61(5), pp.431-442, DOI:http://dx.doi.org/10.14429/dsj.61.117

The patients of the Bristol lunatic asylum in the nineteenth century

There is a wide and impressive historiography about British lunatic asylums in the nineteenth century, the vast majority of which is concerned with their nature and significance. This study does not ignore such subjects but is primarily concerned with the patients, and specifically those of the Bristol Asylum. It asks who they were, what their stories were, how they fared in the asylum, and how the patients’ experience of the asylum changed during the period 1861-1900. It uses a distinctive and multi-faceted methodology, including a comprehensive database - compiled from the asylum records - of all the patients admitted in the nineteenth century. Using pivot tables to analyse the data, the range and nature of the patients admitted according to social class, occupation, age, sex and diagnosis have been accurately assessed. This data dispels suggestions that the patients as a group represented an ‘underclass’. It has also been possible to determine in what ways the asylum changed and how successive medical superintendents altered its nature and ethos. One of these results showed how these various doctors relied on significantly different diagnostic criteria. This affected the lives of the patients and illustrates the somewhat erratic nature of Victorian psychiatric diagnostics.The database was also the starting point for the research here into the patients as individuals. Many aspects of life in the asylum can best be understood by looking at individual cases. The database and other records demonstrate the extent of epilepsy at the asylum, for example, but only individual case studies will show the extent of the suffering and life changing effects consequent upon that illness. Contributing to the telling of these stories is a substantial collection of photographs of the patients. Although their value as evidence is a matter of judgement, it is demonstrated here that they significantly aid our historical imagination – a vital element of social historical practice - in understanding the humanity and suffering of the primary subjects of this study.This study aims, therefore, to be a useful contribution to a growing historiography which offers a more nuanced view of the asylums and brings the lives of patients to the forefront

A new method for generic three dimensional human face modelling for emotional bio-robots

Existing 3D human face modelling methods are confronted with difficulties in applying flexible control over all facial features and generating a great number of different face models. The gap between the existing methods and the requirements of emotional bio-robots applications urges the creation of a generic 3D human face model. This thesis focuses on proposing and developing two new methods involved in the research of emotional bio-robots: face detection in complex background images based on skin colour model and establishment of a generic 3D human face model based on NURBS. The contributions of this thesis are: A new skin colour based face detection method has been proposed and developed. The new method consists of skin colour model for skin regions detection and geometric rules for distinguishing faces from detected regions. By comparing to other previous methods, the new method achieved better results of detection rate of 86.15% and detection speed of 0.4-1.2 seconds without any training datasets. A generic 3D human face modelling method is proposed and developed. This generic parametric face model has the abilities of flexible control over all facial features and generating various face models for different applications. It includes: The segmentation of a human face of 21 surface features. These surfaces have 34 boundary curves. This feature-based segmentation enables the independent manipulation of different geometrical regions of human face. The NURBS curve face model and NURBS surface face model. These two models are built up based on cubic NURBS reverse computation. The elements of the curve model and surface model can be manipulated to change the appearances of the models by their parameters which are obtained by NURBS reverse computation. A new 3D human face modelling method has been proposed and implemented based on bi-cubic NURBS through analysing the characteristic features and boundary conditions of NURBS techniques. This model can be manipulated through control points on the NURBS facial features to build any specific face models for any kind of appearances and to simulate dynamic facial expressions for various applications such as emotional bio-robots, aesthetic surgery, films and games, and crime investigation and prevention, etc

Combining visual recognition and computational linguistics : linguistic knowledge for visual recognition and natural language descriptions of visual content

Extensive efforts are being made to improve visual recognition and semantic understanding of language. However, surprisingly little has been done to exploit the mutual benefits of combining both fields. In this thesis we show how the different fields of research can profit from each other. First, we scale recognition to 200 unseen object classes and show how to extract robust semantic relatedness from linguistic resources. Our novel approach extends zero-shot to few shot recognition and exploits unlabeled data by adopting label propagation for transfer learning. Second, we capture the high variability but low availability of composite activity videos by extracting the essential information from text descriptions. For this we recorded and annotated a corpus for fine-grained activity recognition. We show improvements in a supervised case but we are also able to recognize unseen composite activities. Third, we present a corpus of videos and aligned descriptions. We use it for grounding activity descriptions and for learning how to automatically generate natural language descriptions for a video. We show that our proposed approach is also applicable to image description and that it outperforms baselines and related work. In summary, this thesis presents a novel approach for automatic video description and shows the benefits of extracting linguistic knowledge for object and activity recognition as well as the advantage of visual recognition for understanding activity descriptions.Trotz umfangreicher Anstrengungen zur Verbesserung der die visuelle Erkennung und dem automatischen Verständnis von Sprache, ist bisher wenig getan worden, um diese beiden Forschungsbereiche zu kombinieren. In dieser Dissertation zeigen wir, wie beide voneinander profitieren können. Als erstes skalieren wir Objekterkennung zu 200 ungesehen Klassen und zeigen, wie man robust semantische Ähnlichkeiten von Sprachressourcen extrahiert. Unser neuer Ansatz kombiniert Transfer und halbüberwachten Lernverfahren und kann so Daten ohne Annotation ausnutzen und mit keinen als auch mit wenigen Trainingsbeispielen auskommen. Zweitens erfassen wir die hohe Variabilität aber geringe Verfügbarkeit von Videos mit zusammengesetzten Aktivitäten durch Extraktion der wesentlichen Informationen aus Textbeschreibungen. Wir verbessern überwachtes Training als auch die Erkennung von ungesehenen Aktivitäten. Drittens stellen wir einen parallelen Datensatz von Videos und Beschreibungen vor. Wir verwenden ihn für Grounding von Aktivitätsbeschreibungen und um die automatische Generierung natürlicher Sprache für ein Video zu erlernen. Wir zeigen, dass sich unsere Ansatz auch für Bildbeschreibung einsetzten lässt und das er bisherige Ansätze übertrifft. Zusammenfassend stellt die Dissertation einen neuen Ansatz zur automatische Videobeschreibung vor und zeigt die Vorteile von sprachbasierten Ähnlichkeitsmaßen für die Objekt- und Aktivitätserkennung als auch umgekehrt