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 because of some
limiting factors. Besides different poses, illuminations, occlusions and
expressions, cross-age face is another challenge in face recognition. Different
ages of the same person result in large intra-class variations and aging
process is unavoidable in real world face verification. However, LFW does not
pay much attention on it. Thereby we construct a Cross-Age LFW (CALFW) which
deliberately searches and selects 3,000 positive face pairs with age gaps to
add aging process intra-class variance. Negative pairs with same gender and
race are also selected to reduce the influence of attribute difference between
positive/negative pairs and achieve face verification instead of attributes
classification. We evaluate several metric learning and deep learning methods
on the new database. Compared to the accuracy on LFW, the accuracy drops about
10%-17% on CALFW.