Feature Selection Optimized by Discrete Particle Swarm Optimization for Face Recognition

This paper proposes a new discrete particle swarm optimization (DPSO) algorithm with a multiplicative likeliness enhancement rule for unordered feature selection. In this paper, the pool of features for face recognition are derived from direct fractional-step linear discriminant analysis (DFLDA). Each particle is associated with a subset of features, and their recognition performance on the validation set influences the particle’s fitness with randomness. Features are selected by their assigned likeliness, which is enhanced by the agreement between a particle and its attractors (its previous location, pbest and gbest). The new DPSO double-asserts or triple-asserts the selection if the attractors share common features. The feature selection technique proposed in this paper is a modular procedure and thus can be applied to other features if a separate validation set is available for fitness evaluation. This DPSO algorithm is successfully applied on the FERET database. The recognition performance is improved for both L1 and L2 norm distance metrics. The cumulative matching score (CMS) is improved for higher ranks, which indicates that this performance improvement is beneficial for identification task. In overall comparison, the multiplicative updating rule achieves higher fitness and smaller standard deviation than the additive likeliness enhancement rule.