Deep kinship verification

Mengyin Wang, Zechao Li, Xiangbo Shu, Jingdong Wang, Jinhui Tang

Abstract: To improve the performance of kinship verification, we propose a novel deep kinship verification (DKV) model by integrating excellent deep learning architecture into metric learning. Unlike most existing shallow models based on metric learning for kinship verification, we employ a deep learning model followed by a metric learning formulation to select nonlinear features, which can find the appropriate project space to ensure the margin of negative sample pairs (i.e. parent and child without kinship relation) as large as possible and the margin of positive sample pairs (i.e. parent and child with kinship relation) as small as possible. Experimental results show that our method achieves satisfactory performance on two widely-used benchmarks, i.e. KFW-I and KFW-II.