Abstract
This thesis describes a face verification system that is smart-card-based. The objectives were to identify the key parameters that affect the design of such a system, to investigate die general optimisation problem and test its robustness when each key parameter is optimised. Some of these parameters have been coarsely investigated in the literature in the context of the general face recognition problem. However, the previous work only partially fulfilled the requirements of a smart-card-based system, in which the severe engineering constraints and limitations imposed by smart cards have to be taken into account in the overall design process. To address these problems on the proposed fully localised architecture of the smart card face verification system (SCFVS), the work starts with the selection of the client specific linear discriminant analysis (CS-LDA) algorithm, suitable to be ported to the target platform on which the biometric process can run. Then the main functional parts of the system are presented: face image geometric alignment, photometric normalisation, feature extraction, and on-card verification. Each part consists of a series of basic steps, where the role of each step is fixed. However, the algorithm is systematically varied in some steps to investigate the effect on system performance, and system complexity in terms of speed and memory management. Two major problems have been considered. The first problem are the restrictions that both face verification and smart card technology impose and the second is the extreme complexity of the system, in terms of the number of processing stages and system design parameters. In the simplified search procedure adopted, a number of parameters has been selected out of the complete parameter set involved in a generic SCFVS. This set was recommended by previous main-frame based studies, and deemed to provide acceptable performance. System optimisation in the context of smart card implementation has been conducted starting from those parameters involved in the pre-processing stage of the system, and then those involved in the remaining stages. A joint optimisation framework of the key parameters can also be adopted, assuming that then- effect is independent. Experimental results obtained on a number of publicly available face databases (used to evaluate the system performance) show the significant benefits of this design both in terms of performance and system speed. The different results achieved on different databases indicate that optimum parameters of the system are, to a certain extent, training database dependent.