Abstract
Face recognition finds many important applications in many life sectors and in particular in commercial and law enforcement. This thesis presents two novel methods which make face recognition more practical. In the first method, we propose an attractive solution for efficient face recognition systems that utilize low memory devices. The new technique applies the principal component analysis to the binarized phase spectrum of the Fourier transform of the covariance matrix constructed from the MPEG-7 Fourier Feature Descriptor vectors of the face images. Most of the algorithms proposed for face recognition are computationally exhaustive and hence they can not be used on devices constrained with limited memory; hence our method may play an important role in this area. The second method presented in this thesis proposes a new approach for efficient face representation and recognition by finding the best location component-based linear discriminant analysis. In this regard, the face image is decomposed into a number of components of certain size. Then the proposed scheme finds the best representation of the face image in most efficient way, taking into consideration both the recognition rate and the processing time. Note that the effect of the variation in a face image, when it is taken as a whole, is reduced when it is divided into components. As a result the performance of the system is enhanced. This method should find applications in systems requiring very high recognition and verification rates. Further, we demonstrate a solution to the problem of face occlusion using this method. The experimental results show that both proposed methods enhance the performance of the face recognition system and achieve a substantial saving in the computation time when compared to other known methods. It will be shown that the two proposed methods are very attractive for a wide range of applications for face recognition.