Kinship verification via correlation calculation-based multi-task learning

Previous studies have demonstrated that metric learning approaches yield remarkable performance in the field of kinship verification. Nevertheless, a prevalent limitation of most existing methods lies in their over-reliance on learning exclusively from specified types of given kin data, which frequently results in information isolation. Although generative-based metric learning methods present potential solutions to this problem, they are hindered by substantial computational costs. To address these challenges, this paper proposes a novel correlation calculation-based multi-task learning (CCMTL) method specifically designed for kinship verification. It has been observed that kin members often exhibit a high degree of similarity in key facial organs, such as eyes, mouths, and noses. Given this similarity, similar facial features between kin members with different kin relationships frequently demonstrate certain correlations. Inspired by this observation, our proposed method aims to learn a set of metrics by leveraging both the specified kinship data and the correlations among various kinship types. These correlations are determined through an in-depth investigation of the spatial distribution relationship between the specified kinship data and other kinship types. Furthermore, we develop an efficient algorithm within the multi-task learning framework that integrates correlation exploitation with metric learning. This innovative approach effectively resolves the issue of information isolation while minimizing computational overhead. Extensive experimental validation conducted on the KinFaceW dataset demonstrates that the proposed CCMTL method achieves superior or comparable results to those of existing methods.