  Unconstrained face recognition performance evaluations have traditionally
focused on Labeled Faces in the Wild (LFW) dataset for imagery and the
YouTubeFaces (YTF) dataset for videos in the last couple of years. Spectacular
progress in this field has resulted in saturation on verification and
identification accuracies for those benchmark datasets. In this paper, we
propose a unified learning framework named Transferred Deep Feature Fusion
(TDFF) targeting at the new IARPA Janus Benchmark A (IJB-A) face recognition
dataset released by NIST face challenge. The IJB-A dataset includes real-world
unconstrained faces from 500 subjects with full pose and illumination
variations which are much harder than the LFW and YTF datasets. Inspired by
transfer learning, we train two advanced deep convolutional neural networks
(DCNN) with two different large datasets in source domain, respectively. By
exploring the complementarity of two distinct DCNNs, deep feature fusion is
utilized after feature extraction in target domain. Then, template specific
linear SVMs is adopted to enhance the discrimination of framework. Finally,
multiple matching scores corresponding different templates are merged as the
final results. This simple unified framework exhibits excellent performance on
IJB-A dataset. Based on the proposed approach, we have submitted our IJB-A
results to National Institute of Standards and Technology (NIST) for official
evaluation. Moreover, by introducing new data and advanced neural architecture,
our method outperforms the state-of-the-art by a wide margin on IJB-A dataset.
