Dynamic gesture recognition using transformation invariant hand shape recognition

In this thesis a detailed framework is presented for accurate real time gesture recognition. Our approach to develop a hand-shape classifier, trained using computer animation, along with its application in dynamic gesture recognition is described. The system developed operates in real time and provides accurate gesture recognition. It operates using a single low resolution camera and operates in Matlab on a conventional PC running Windows XP. The hand shape classifier outlined in this thesis uses transformation invariant subspaces created using Principal Component Analysis (PCA). These subspaces are created from a large vocabulary created in a systematic maimer using computer animation. In recognising dynamic gestures we utilise both hand shape and hand position information; these are two o f the main features used by humans in distinguishing gestures. Hidden Markov Models (HMMs) are trained and employed to recognise this combination of hand shape and hand position features. During the course o f this thesis we have described in detail the inspiration and motivation behind our research and its possible applications. In this work our emphasis is on achieving a high speed system that works in real time with high accuracy

Using a serious game to assess spatial memory in children and adults

Short-term spatial memory has traditionally been assessed using visual stimuli, but not auditory stimuli. In this paper, we design and test a serious game with auditory stimuli for assessing short-term spatial memory. The interaction is achieved by gestures (by raising your arms). The auditory stimuli are emitted by smart devices placed at different locations. A total of 70 participants (32 children and 38 adults) took part in the study. The outcomes obtained with our game were compared with traditional methods. The results indicated that the outcomes in the game for the adults were significantly greater than those obtained by the children. This result is consistent with the assumption that the ability of humans increases continuously during maturation. Correlations were found between our game and traditional methods, suggesting its validity for assessing spatial memory. The results indicate that both groups easily learn how to perform the task and are good at recalling the locations of sounds emitted from different positions. With regard to satisfaction with our game, the mean scores of the children were higher for nearly all of the questions. The mean scores for all of the questions, except one, were greater than 4 on a scale from 1 to 5. These results show the satisfaction of the participants with our game. The results suggest that our game promotes engagement and allows the assessment of spatial memory in an ecological way

Computational Models for the Automatic Learning and Recognition of Irish Sign Language

This thesis presents a framework for the automatic recognition of Sign Language sentences. In previous sign language recognition works, the issues of; user independent recognition, movement epenthesis modeling and automatic or weakly supervised training have not been fully addressed in a single recognition framework. This work presents three main contributions in order to address these issues. The first contribution is a technique for user independent hand posture recognition. We present a novel eigenspace Size Function feature which is implemented to perform user independent recognition of sign language hand postures. The second contribution is a framework for the classification and spotting of spatiotemporal gestures which appear in sign language. We propose a Gesture Threshold Hidden Markov Model (GT-HMM) to classify gestures and to identify movement epenthesis without the need for explicit epenthesis training. The third contribution is a framework to train the hand posture and spatiotemporal models using only the weak supervision of sign language videos and their corresponding text translations. This is achieved through our proposed Multiple Instance Learning Density Matrix algorithm which automatically extracts isolated signs from full sentences using the weak and noisy supervision of text translations. The automatically extracted isolated samples are then utilised to train our spatiotemporal gesture and hand posture classifiers. The work we present in this thesis is an important and significant contribution to the area of natural sign language recognition as we propose a robust framework for training a recognition system without the need for manual labeling

Hand gesture modelling and recognition involving changing shapes and trajectories, using a Predictive EigenTracker

We present a novel eigenspace-based framework to model a dynamic hand gesture that incorporates both hand shape as well as trajectory information. We address the problem of choosing a gesture set that models an upper bound on gesture recognition efficiency. We show encouraging experimental results on a such a representative set. (c) 2006 Elsevier B.V. All rights reserved

An evaluation of the theory and the practice of terrorist profiling in the identification of terrorist characteristics

A key trend in laws and policies aimed at combatting terrorism is the increasing use of policing strategies that allow law enforcement officers anticipate risk so that they can engage in preventing, interrupting and prosecuting those suspected of terrorism offences before their commission. One such pre-emptive policing strategy is the use of terrorist profiling. The rationale underpinning terrorist profiling is to allow law enforcement officers identify those likely to involved in terrorism or its associated activities so that law enforcement officers can prevent, interrupt and prosecute suspects before an act of terrorism. The use of terrorist profiling is highly controversial given that its use has been perceived as being unlawful. Previous attempts to analyse terrorist profiling has tended to rely solely on human rights law as the analytical lens to evaluate the usefulness and lawfulness of terrorist profiling. The discussion in this thesis argues that the effectiveness and usefulness of terrorist profiling should only be undertaken by deconstructing the profiling process so as to allow a thorough examination of the phenomenon of terrorist profiling. As a result, the discussion in this thesis establishes two analytical lenses as the basis to systematically examine terrorist profiling. Firstly, the discussion develops an effectiveness framework that examines the construction of terrorist profiles separately from the application of terrorist profiles. Secondly, the discussion also draws upon criminal profiling methodologies and approaches as the basis to evaluate different manifestations of terrorist profiling. These analytical lenses are used to conduct a taxonomy on different manifestations of terrorist profiling so as to systematically evaluate their usefulness as a law enforcement tool to predict likely terrorist characteristics