novel face recognition transformational model and its inherent and optimal classification through a computationally efficient statistical algorithm

This thesis is concerned with the development of a new face recognition method that has a high recognition performance and is computationally efficient, so that it can be applied to real time processes. A background research is presented, summarizing the most dominant face recognition methods, with an emphasis to the most popular statistical method, the 'Eigenfaces'. Initially, a new algorithm is developed based only on the computational efficiency criterion. It is simulated, and criterions for achieving higher recognition rates are experimentally and theoretically determined. A new space transform is introduced, which enhances the algorithm's recognition capabilities. Its optimum classification measure is mathematically proven to be one that is inherently provided by the new face recognition algorithm. Finally, the developed method is evaluated, and experimentally compared against the 'Eigenfaces' method, using face data.