Exploring regularized feature selection for person specific face verification

摘要

In this paper, we explore the regularized feature selection method for person specific face verification in unconstrained environments. We reformulate the generalization of the single-task sparsity-enforced feature selection method to multi-task cases as a simultaneous sparse approximation problem. We also investigate two feature selection strategies in the multi-task generalization based on the positive and negative feature correlation assumptions across different persons. Simultaneous orthogonal matching pursuit (SOMP) is adopted and modified to solve the corresponding optimization problems. We further proposed a named simultaneous subspace pursuit (SSP) methods which generalize the subspace pursuit method to solve the corresponding optimization problems. The performance of different feature selection strategies and different solvers for face verification are compared on the challenging LFW face database. Our experimental results show that 1) the selected subsets based on positive correlation assumption are more effective than those based on the negative correlation assumption; 2) the OMP-based solvers outperform SP-based solvers in terms of feature selection and 3) the regularized methods with OMP-based solvers can outperform state-of-the-art feature selection methods.