WPFP-PCA: weighted parallel fixed point PCA Face recognition

: 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 (EVD) 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 EVD 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 advance in technology of multicore 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 (ED) optimization. These stages include parallel pre-processor a...

• Type: Journal
• Year: 2016
• Where: IAJIT
• Authors: Chakchai So-In, Kanokmon Rujirakul
• Keywords: Biometrics, IAJIT 2016