參考：http://www.whdeng.cn/CPLFW/index.html?reload=true
一、概述
人臉數據的驗證集用於訓練過程中對模型進行精度驗證，常用的人臉數據驗證集有lfw，cfp_fp和agedb30。
我們以cplfw數據爲例來進行人臉數據驗證集的製作
Labeled Faces in the Wild (LFW) database has been widely utilized as the benchmark of unconstrained face verification and due to big data driven machine learning methods, the performance on the database approaches nearly 100%. However, we argue that this accuracy may be too optimistic. Besides different illuminations, occlusions and expressions, cross-pose face is another challenge in face recognition yet LFW does not pay much attention on it. Thereby we construct a Cross-Pose LFW (CPLFW) which deliberately searches and selects 3,000 positive face pairs with pose difference to add pose variation to intra-class variance. Negative pairs with same gender and race are also selected to reduce the influence of attribute difference between pos