Face Recognition Benchmark with ID Photos

Abstract

With the development of deep neural networks, researchers have developed lots of algorithms related to face and achieved comparable results to human-level performance on several databases. However, few feature extraction models work well in the real world when the subject which is to be recognized has limited samples, for example, only one ID photo can be obtained before the face recognition task. To our best knowledge, there is no face database which contains ID photos and pictures from the real world for a subject simultaneously. To fill this gap, we collected 100 celebrities’ ID photos and their about 1000 stills or life pictures and formed a face database called FDID. Besides, we proposed a novel face recognition algorithm and evaluated it with this new database on the real-life videos.