Kinship verification is an interesting and important problem in the fields of computer vision. In practice, the biggest obstacle in kinship verification is that the representation capability of extracted features may not be powerful due to the significant differences between facial images of family members. To effectively address this problem, we propose a semi-coupled synthesis and analysis dictionary pair learning (SSADL) approach, which can reduce the differences between facial images. Specifically, SSADL jointly learns two view-specific synthesis-analysis dictionary pairs as well as a mapping matrix from the training data of parent and child, with which, the heterogeneous facial images of parent and child can be transformed into coding coefficients of the same subspace, such that the kinship verification task can be conducted using the coding coefficients. Besides, we also design a hard sample based coefficient discriminant term to ensure that the obtained coefficients own favorable discriminability. Experimental results on several publicly used benchmarks show the effectiveness of our proposed approach.

中文翻译：

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半耦合综合与分析字典对学习的亲缘关系验证

亲属关系验证是计算机视觉领域中一个有趣且重要的问题。在实践中，亲属关系验证的最大障碍是，由于家庭成员的面部图像之间存在显着差异，因此提取的特征的表示能力可能不够强大。为了有效解决这个问题，我们提出了一种半耦合的合成和分析字典对学习（SSADL）方法，该方法可以减少人脸图像之间的差异。具体来说，SSADL从父母和孩子的训练数据中联合学习两个特定于视图的综合分析字典对以及一个映射矩阵，利用它们，父母和孩子的异质面部图像可以转换为相同子空间的编码系数，这样就可以使用编码系数来执行亲属关系验证任务。此外，我们还设计了一个基于硬样本的系数判别项，以确保获得的系数具有良好的判别力。在几个公开使用的基准上的实验结果表明了我们提出的方法的有效性。