Humans can recognise individuals in many different situations. Automated vision-based biometric systems, which identify individuals from an image of a particular physical feature, aspire to a similar level of performance but currently have to impose constraints to achieve satisfactory recognition rates. These include limitations on the background of the image in which a feature is located, the lighting on the feature, its degree of occlusion, its viewed angle, and the properties of the camera that captures it. The computational cost of any recognition system is also an issue. This thesis examines ways of reducing such constraints. Its particular focus is the recognition of individuals from the unique signature provided by their ears. Speciffically, the work develops techniques to support a hypothesis that: The constraints on the use of ear-based biometric systems can be relaxed significantly through the introduction of robust recognition techniques. Two novel techniques designed to improve robustness are described: (i) a fully automated 2D recognition system to reduce sensitivity to noise and occlusion; and (ii) the use of a 3D model to allow for variations in both pose and lighting; The thesis begins by summarising current progress in the general field of biometrics and in the associated techniques for robust recognition. Each technique is then described in successive chapters, identifying related work, explaining the technique in detail and evaluating its performance. Future work will focus on developing algorithms to enable the 3D model to be accurately fitted to images. A number of developments in this area are outlined in the appendix. While these techniques have been developed for ear recognition they also contribute to the general research challenge of recognising any object in any environment
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:539034 |
| Date | January 2011 |
| Creators | Bustard, John |
| Contributors | Nixon, Mark |
| Publisher | University of Southampton |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | https://eprints.soton.ac.uk/183227/ |
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