Kinship verification refers to comparing similarities between two different individuals through their facial images. In this context, feature descriptors play a crucial role, and few feature descriptors are present in literature to extract kin features from facial images. In this paper, we propose a binary cross-coupled discriminant analysis $\left(B{C}^{2}DA\right)$ based feature descriptor which is able to extract effective kin features from input facial image pairs. This method reduces the discrimination between kin pairs at the feature extraction stage itself. $B{C}^{2}DA$ converts original kin image pairs to encoded image pairs to reduce the discrimination between them. To make better use of tri-subject kin relations, we further propose multi cross-coupled discriminant analysis $\left(M{C}^{2}DA\right)$. This method reduces the discrimination between child and both parents’ images at the feature extraction stage. Extensive experiments were conducted on six kinship datasets such as KinfaceW-I/II, Cornell, FIW, TSKinface UBKinface to show the efficacy of the proposed algorithm.

亲属关系验证是指通过他们的面部图像比较两个不同个体之间的相似性。在这种情况下，特征描述符起着至关重要的作用，文献中很少有特征描述符用于从面部图像中提取亲属特征。在本文中，我们提出了一种二元交叉耦合判别分析$\left(乙C^{2}D\mathrm{一个}\right)$基于特征描述符，能够从输入的面部图像对中提取有效的亲属特征。该方法在特征提取阶段本身减少了亲属对之间的区分。$乙C^{2}D\mathrm{一个}$将原始亲属图像对转换为编码图像对以减少它们之间的区分。为了更好地利用三主体亲属关系，我们进一步提出了多交叉耦合判别分析$\left(米C^{2}D\mathrm{一个}\right)$. 该方法在特征提取阶段减少了孩子和父母双方图像之间的区别。在 KinfaceW-I/II、Cornell、FIW、TSKinface UBKinface 等六个亲属数据集上进行了广泛的实验，以展示所提出算法的有效性。