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Tri-Subject Kinship Verification: Understanding the Core of A Family

机译：三主体亲属关系验证：了解家庭的核心

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摘要

One major challenge in computer vision is to go beyond the modeling of individual objects and to investigate the bi- (one-versus-one) or tri- (one-versus-two) relationship among multiple visual entities, answering such questions as whether a child in a photo belongs to the given parents. The child-parents relationship plays a core role in a family, and understanding such kin relationship would have a fundamental impact on the behavior of an artificial intelligent agent working in the human world. In this work, we tackle the problem of one-versus-two (tri-subject) kinship verification and our contributions are threefold: 1) a novel relative symmetric bilinear model (RSBM) is introduced to model the similarity between the child and the parents, by incorporating the prior knowledge that a child may resemble one particular parent more than the other; 2) a spatially voted method for feature selection, which jointly selects the most discriminative features for the child-parents pair, while taking local spatial information into account; and 3) a large-scale tri-subject kinship database characterized by over 1,000 child-parents families. Extensive experiments on KinFaceW, Family101, and our newly released kinship database show that the proposed method outperforms several previous state of the art methods, while could also be used to significantly boost the performance of one-versus-one kinship verification when the information about both parents are available.
机译：计算机视觉的一个主要挑战是超越对单个对象的建模，并研究多个视觉实体之间的双向（一对一）或双向（一对二）关系，回答诸如以下问题：照片中的孩子属于给定的父母。儿童与父母的关系在家庭中起着核心作用，了解这种亲属关系将对在人类世界中工作的人工智能代理的行为产生根本影响。在这项工作中，我们解决了一对二（三主体）亲属验证的问题，我们的贡献有三点：1）引入了一种新颖的相对对称双线性模型（RSBM）来建模孩子与父母之间的相似性，通过结合先验知识，即一个孩子比另一个父母更像一个特定的父母； 2）一种空间投票的特征选择方法，该方法在考虑局部空间信息的情况下共同为儿童-父母对选择最具区分性的特征； 3）拥有三千多个亲子家庭的大型三族亲属数据库。在KinFaceW，Family101和我们新发布的亲属关系数据库上进行的大量实验表明，所提出的方法优于以前的几种最新方法，同时当有关两者的信息都可以用于显着提高一对一亲属关系验证的性能时父母有空。

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