Kinship verification via correlation calculation-based multi-task learning.

ABSTRACT: 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 t

SUBMITTER: Qin X 

PROVIDER: S-EPMC12419595 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature