Face Recognition on Heterogeneous Architecture using Parallel Computing Paradigms

Abstract

Face recognition applications are widely used in different areas, specifically, in security and biometrics. The decision often should be highly accurate and fast. Principle Component Analysis is a feature extraction algorithm used in facial recognition applications by projecting images on a new face-space. It is mainly applied to reduce the dimensionality of the image. However, PCA consumes a lot of processing time due to its high intensive computation nature. To overcome the single computing systems limitations, different parallel processing paradigms are used to accelerate the process. In this thesis, two face recognition scenarios are implemented using different parallel programming memory architectures. First, we show how a cluster of supercomputers can be used to accelerate a face recognition system. The work focuses on speeding either the training or testing phase of PCA. In addition, the suggested environment is dynamic to different numbers of supercomputers. Experiment