Covariate factor mitigation techniques for robust gait recognition

The human gait is a discriminative feature capable of recognising a person by their unique walking manner. Currently gait recognition is based on videos captured in a controlled environment. These videos contain challenges, termed covariate factors, which affect the natural appearance and motion of gait, e.g. carrying a bag, clothing, shoe type and time. However gait recognition has yet to achieve robustness to these covariate factors. To achieve enhanced robustness capabilities, it is essential to address the existing gait recognition limitations. Specifically, this thesis develops an understanding of how covariate factors behave while a person is in motion and the impact covariate factors have on the natural appearance and motion of gait. Enhanced robustness is achieved by producing a combination of novel gait representations and novel covariate factor detection and removal procedures. Having addressed the limitations regarding covariate factors, this thesis achieves the goal of robust gait recognition. Using a skeleton representation of the human figure, the Skeleton Variance Image condenses a skeleton sequence into a single compact 2D gait representation to express the natural gait motion. In addition, a covariate factor detection and removal module is used to maximise the mitigation of covariate factor effects. By establishing the average pixel distribution within training (covariate factor free) representations, a comparison against test (covariate factor) representations achieves effective covariate factor detection. The corresponding difference can effectively remove covariate factors which occur at the boundary of, and hidden within, the human figure.The Engineering and Physical Sciences Research Council (EPSRC

Angular feature extraction and ensemble classification method for 2D, 2.5D and 3D face recognition.

It has been recognised that, within the context of face recognition, angular separation between centred feature vectors is a useful measure of dissimilarity. In this thesis we explore this observation in more detail and compare and contrast angular separation with the Euclidean, Manhattan and Mahalonobis distance metrics. This is applied to 2D, 2.5D and 3D face images and the investigation is done in conjunction with various feature extraction techniques such as local binary patterns (LBP) and linear discriminant analysis (LDA). We also employ error-correcting output code (ECOC) ensembles of support vector machines (SVMs) to project feature vectors non-linearly into a new and more discriminative feature space. It is shown that, for both face verification and face recognition tasks, angular separation is a more discerning dissimilarity measure than the others. It is also shown that the effect of applying the feature extraction algorithms described above is to considerably sharpen and enhance the ability of all metrics, but in particular angular separation, to distinguish inter-personal from extra-personal face image differences. A novel technique, known as angularisation, is introduced by which a data set that is well separated in the angular sense can be mapped into a new feature space in which other metrics are equally discriminative. This operation can be performed separately or it can be incorporated into an SVM kernel. The benefit of angularisation is that it allows strong classification methods to take advantage of angular separation without explicitly incorporating it into their construction. It is shown that the accuracy of ECOC ensembles can be improved in this way. A further aspect of the research is to compare the effectiveness of the ECOC approach to constructing ensembles of SVM base classifiers with that of binary hierarchical classifiers (BHC). Experiments are performed which lead to the conclusion that, for face recognition problems, ECOC yields greater classification accuracy than the BHC method. This is attributed primarily to the fact that the size of the training set decreases along a path from the root node to a leaf node of the BHC tree and this leads to great difficulties in constructing accurate base classifiers at the lower nodes

Ubiquitous Computing

The aim of this book is to give a treatment of the actively developed domain of Ubiquitous computing. Originally proposed by Mark D. Weiser, the concept of Ubiquitous computing enables a real-time global sensing, context-aware informational retrieval, multi-modal interaction with the user and enhanced visualization capabilities. In effect, Ubiquitous computing environments give extremely new and futuristic abilities to look at and interact with our habitat at any time and from anywhere. In that domain, researchers are confronted with many foundational, technological and engineering issues which were not known before. Detailed cross-disciplinary coverage of these issues is really needed today for further progress and widening of application range. This book collects twelve original works of researchers from eleven countries, which are clustered into four sections: Foundations, Security and Privacy, Integration and Middleware, Practical Applications

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https://egrove.olemiss.edu/aicpa_guides/1610/thumbnail.jp