the paper presents a new method color MS-BSIF learning and MS-LBP for the kinship verification is the machine's ability to identify the genetic and blood the relationship and its degree between the facial images of humans. Facial verification of kinship refers to the task of training a machine to recognize the blood relationship between a pair of faces parent and non-parent (verification) based on features extracted from facial images, and determining the exact type or degree of this genetic relationship. We use the LBP and color BSIF learning features for the comparison and the TXQDA method for dimensionality reduction and data classification. We let's test the kinship facial verification application is namely the kinface Cornell database. This system improves the robustness of learning while controlling efficiency. The experimental results obtained and compared to other methods have proven the reliability of our framework and surpass the performance of other state-of-the-art techniques.

翻译：本文提出了一种基于颜色MS-BSIF学习与MS-LBP的亲属关系验证新方法，该方法旨在赋予机器识别人类面部图像中遗传与血缘关系及其程度的能力。面部亲属关系验证是指基于面部图像提取的特征，训练机器识别一对人脸（父母与非父母）间的血缘关系（即验证），并确定这种遗传关系的具体类型或程度。我们采用LBP与颜色BSIF学习特征进行对比，并利用TXQDA方法进行降维与数据分类。在KinFace Cornell数据库上对该亲属关系面部验证应用进行了测试。本系统在控制效率的同时提升了学习的鲁棒性。实验结果表明，相较于其他方法，我们的框架可靠性更高，且性能超越了当前最先进的技术。