KINMIX: A Data Augmentation Approach for Kinship Verification

In this paper, we propose a KinMix method to generate positive samples in the feature space for facial kinship verification. Unlike most existing data augmentation methods, we generate samples at the feature level instead of the original image level for data augmentation. Specifically, we use a pair of features with a kin relationship to construct a feature space instead of a single image, which aims to maintain the clustering results of features. We assume that a pair of kinship features lie in the same feature space, so that the generated feature remains the same kin relationship if it is sampled from the feature space. We use a linear sampling method to generate positive samples with the kin relationship. We evaluate our method on two widely used kinship datasets: KinFaceW-I and KinFaceW-II, and our experimental results are presented to show the effectiveness of the proposed approach.