Kinship verification from facial images has been recognized as an emerging yet challenging technique in many potential computer vision applications. In this paper, we propose a novel cross-generation feature interaction learning (CFIL) framework for robust kinship verification. Particularly, an effective collaborative weighting strategy is constructed to explore the characteristics of cross-generation relations by corporately extracting features of both parents and children image pairs. Specifically, we take parents and children as a whole to extract the expressive local and non-local features. Different from the traditional works measuring similarity by distance, we interpolate the similarity calculations as the interior auxiliary weights into the deep CNN architecture to learn the whole and natural features. These similarity weights not only involve corresponding single points but also excavate the multiple relationships cross points, where local and non-local features are calculated by using these two kinds of distance measurements. Importantly, instead of separately conducting similarity computation and feature extraction, we integrate similarity learning and feature extraction into one unified learning process. The integrated representations deduced from local and non-local features can comprehensively express the informative semantics embedded in images and preserve abundant correlation knowledge from image pairs. Extensive experiments demonstrate the efficiency and superiority of the proposed model compared to some state-of-the-art kinship verification methods.

翻译：在很多潜在的计算机视觉应用中,通过面部图像进行亲子关系核实被认为是一种新兴但具有挑战性的技术。在本文中,我们提出一个新的跨代地物互动学习框架(CFIL),以进行稳健的亲情核查。特别是,通过公司提取父母和儿童图像配对的特征,设计了一个有效的协作加权战略,以探索跨代关系的特点。具体地说,我们把父母和儿童作为一个整体,以提取表象性的本地和非本地特征。不同于测量距离相似性的传统工程,我们将类似性计算作为内分量计算方法,作为内部辅助重量,在深层CNN结构中进行,以了解整体和自然特征。这些相似性加权不仅涉及相应的单一点,而且还挖掘了多重关系交叉点,在这些点上,当地和非本地特征是通过使用这两种远程测量方法计算的。重要的是,我们把相似性学习和特征提取纳入一个统一的学习过程。从地方和非本地性和非本地性特征中得出的综合表述可以全面表达图像中所含的信息性语义,并保存一些与图像组合的丰富性相关知识。广泛的实验方法将展示国家性和优越性。