PFP-PCA: Parallel Fixed Point PCA Face Recognition

With a high computational complexity of Eigenvector/Eigenvalue calculation, especially with a large database, of a traditional face recognition system, PCA, this paper proposes an alternative approach to utilize a fixed point algorithm for EVD stage optimization. We also proposed the optimization to reduce the complexity during the high computation stage, covariance matrix manipulation. In addition, the feasibility to enhance the speed-up over a single-core computation, parallelism, was investigated on the huge matrix calculation on both grayscale and RGB images. This mechanism, the so-called Parallel Fixed Point PCA (PFP-PCA), results in higher accuracy and lower complexity comparing to the traditional PCA leading to a high speed face recognition system.