Ensemble similarity learning for kinship verification from facial images in the wild

A new method for kinship verification from biometrics.A sparse similarity function to model the relative characteristics of kin data.A new formulation combining the merits of ensemble learning and similarity learning.The method has superior verification rate but low computational cost. Kin relationship has been well investigated in psychology community over the past decades, while kin verification using facial images is relatively new and challenging problem in biometrics society. Recently, it has attracted substantial attention from biometrics society, mainly motivated by the relative characteristics that children generally resemble their parents more than other persons with respect to facial appearance. Unlike most previous supervised metric learning methods focusing on learning the Mahalanobis distance metric for kin verification, we propose in this paper a new Ensemble similarity learning (ESL) method for this challenging problem. We first introduce a sparse bilinear similarity function to model the relative characteristics encoded in kin data. The similarity function parameterized by a diagonal matrix enjoys the superiority in computational efficiency, making it more practical for real-world high-dimensional kinship verification applications. Then, ESL learns from kin dataset by generating an ensemble of similarity models with the aim of achieving strong generalization ability. Specifically, ESL works by best satisfying the constraints (typically triplet-based) derived from the class labels on each base similarity model, while maximizing the diversity among the base similarity models. Experiments results demonstrate that our method is superior to some state-of-the-art methods in terms of both verification rate and computational efficiency.