Pixel Consistency, K-Tournament Selection, and Darwinian-Based Feature Extraction

In this paper, we present a two-stage process for developing feature extractors (FEs) for facial recognition. In this process, a genetic algorithm is used to evolve a number of local binary patterns (LBP) based FEs with each FE consisting of a number of (possibly) overlapping patches from which features are extracted from an image. These FEs are then overlaid to form what is referred to as a hyper FE. The hyper FE is then used to create a probability distribution function (PDF). The PDF is a two dimensional matrix that records the number of patches within the hyper FE that a particular pixel is contained within. Thus, the PDF matrix records the consistency of pixels contained within patches of the hyper FE. Darwinian-based FEs (DFEs) are then constructed by sampling the PDF via k-tournament selection to determine which pixels of a set of images will be used in extract features from. Our results show that DFEs have a higher recognition rate as well as a lower computational complexity than other LBP-based feature extractors.