Genetic Algorithm for Weight Optimization in Descriptor based Face Recognition Methods

摘要

This paper presents a novel algorithm for weight optimization in descriptor based face recognition methods. We aim at the local texture features that are currently very popular in the face recognition (FR) field. Common concept in such methods is creating histograms of the operator values in rectangular image regions and concatenating them into one large vector called histogram sequence (HS). Usually the facial regions are given equal weight which does not correspond with the reality. We deal with this issue in this work and propose a novel method that optimizes the weights of the regions. The optimization method is based on a genetic algorithm (GA). We test the method together with the local binary patterns (LBP) and patterns of oriented edge magnitudes (POEM) descriptors. We evaluate our algorithms on two real-world corpora: Unconstrained facial images (UFI) database and FaceScrub database. The evaluation results show that the weighted methods outperform the non-weighted ones. The best achieved scores are 68.93% on the UFI database and 57.81% on the FaceScrub database.