亲子关系验证的端到端深度度量学习技术OA

As an important application in the field of identification,parentage verification is in increasing demand in social life and legal affairs.Traditional biometric recognition methods often face challenges such as insufficient accuracy when dealing with complex facial similarities between family relatives.In recent years,deep learning,especially metric learning technologies,has shown strong potential in dealing with image similarity problems.Therefore,this paper discusses parent-child relationship verification technology based on end-to-end deep metric learning,and proposes a new model framework and optimization strategy,aiming to directly learn more discriminant parent-child feature representations and similarity measures from data.Criteria,in the expectation of significantly improving the accuracy and robustness of parent-child relationship discrimination,and providing more reliable technical support for related applications.