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.

中文翻译：

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基于跨代特征交互学习的亲属关系验证


在许多潜在的计算机视觉应用中，通过面部图像进行亲属关系验证已被认为是一种新兴但具有挑战性的技术。在本文中，我们提出了一种新颖的跨代特征交互学习（CFIL）框架，用于稳健的亲属关系验证。特别是，通过共同提取父母和孩子图像对的特征，构建有效的协作加权策略来探索跨代关系的特征。具体来说，我们将父母和孩子作为一个整体来提取表达的局部和非局部特征。与传统的通过距离测量相似度的工作不同，我们将相似度计算作为内部辅助权重插入到深度CNN架构中，以学习整体和自然的特征。这些相似性权重不仅涉及对应的单点，还挖掘多重关系交叉点，其中局部和非局部特征是利用这两种距离测量来计算的。重要的是，我们没有将相似度计算和特征提取分开进行，而是将相似度学习和特征提取集成到一个统一的学习过程中。从局部和非局部特征推导的综合表示可以全面表达图像中嵌入的信息语义，并保留图像对中丰富的相关知识。大量的实验证明了与一些最先进的亲属关系验证方法相比，所提出的模型的效率和优越性。