WPFP-PCA : weighted parallel fixed point PCA face recognition

Abstract EN

Principal Component Analysis (PCA) is one of the feature extraction techniques, commonly used in human facial recognition systems.

PCA yields high accuracy rates when requiring lower dimensional vectors; however, the computation during covariance matrix and eigenvalue decomposition stages leads to a high degree of complexity that corresponds to the increase of datasets.

Thus, this research proposes an enhancement to PCA that lowers the complexity by utilizing a Fixed Point (FP) algorithm during the eigenvalue decomposition stage.

To mitigate the effect of image projection variability, an adaptive weight was also employed added to FP-PCA called wFP-PCA.

To further improve the system, the advances in technology of multi-core architectures allows for a degree of parallelism to be investigated in order to utilize the benefits of matrix computation parallelization on both feature extraction and classification with weighted Euclidian Distance optimization.

These stages include parallel pre-processor and their combinations, called weighed Parallel Fixed Point PCA wPFP-PCA.

When compared to a traditional PCA and its derivatives which includes our first enhancement wFP-PCA, the performance of wPFP-PCA is very positive, especially in higher degree of recognition precisions, i.

e., 100 % accuracy over the other systems as well as the increase of computational speed-ups.