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Unconstrained face recognition performance evaluations have traditionallyfocused on Labeled Faces in the Wild (LFW) dataset for imagery and theYouTubeFaces (YTF) dataset for videos in the last couple of years. Spectacularprogress in this field has resulted in saturation on verification andidentification accuracies for those benchmark datasets. In this paper, wepropose a unified learning framework named Transferred Deep Feature Fusion(TDFF) targeting at the new IARPA Janus Benchmark A (IJB-A) face recognitiondataset released by NIST face challenge. The IJB-A dataset includes real-worldunconstrained faces from 500 subjects with full pose and illuminationvariations which are much harder than the LFW and YTF datasets. Inspired bytransfer learning, we train two advanced deep convolutional neural networks(DCNN) with two different large datasets in source domain, respectively. Byexploring the complementarity of two distinct DCNNs, deep feature fusion isutilized after feature extraction in target domain. Then, template specificlinear SVMs is adopted to enhance the discrimination of framework. Finally,multiple matching scores corresponding different templates are merged as thefinal results. This simple unified framework exhibits excellent performance onIJB-A dataset. Based on the proposed approach, we have submitted our IJB-Aresults to National Institute of Standards and Technology (NIST) for officialevaluation. 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.

A Good Practice Towards Top Performance of Face Recognition: Transferred Deep Feature Fusion