Sparse similarity metric learning for kinship verification

Abstract

Metric learning technique learns a linear transformation of the given training data which can significantly promote the performance of a prediction task, such as kinship verification. However, many of the existing metric learning methods do not explicitly regularize for sparsity or low-rank, which in practice usually results in high-rank solutions that are not only time-consuming but also tend to overfitting. In addition, some methods simply neglect the positive semidefinite (PSD) constraint causing the learned metric to be potentially noisy. In this paper, we propose an effective sparse similarity metric learning (SSML) method which enforces both the group sparsity and the PSD constraints on the learned similarity matrix for kinship verification. In order to solve the proposed optimization problem efficiently, we successfully apply the alternating direction method of multipliers (ADMM) to obtain the optimal solution. Experimental results demonstrate that the proposed method achieves competitive results compared with other state-of-the-art metric learning methods on widely used kinship datasets.