Discriminative kernel-based metric learning for face verification

Abstract

This paper outlines a simplistic formulation for doublet constrained discriminative metric learning framework for face verification. The Mahalanobis distance metric of the framework is formulated by leveraging the within-class scatter matrix of the doublet and a quadratic kernel function. Unlike existing metric learning methods, the proposed framework admits efficient solution attributed to the convexity nature of the kernel machines. We demonstrate three realizations of the proposed framework based on the well-known kernel machine instances, namely Support Vector Machine, Kernel Ridge Regression and Least Squares Support Vector Machine. Due to wide availability of off-the-shelf kernel learner solvers, the proposed method can be easily trained and deployed. We evaluate the proposed discriminative kernel-based metric learning with two types of face verification setup: standard and unconstrained face verification through three benchmark datasets. The promising experimental results corroborate the feasibility and robustness of the proposed framework.