  Although deep learning approaches have achieved performance surpassing humans
for still image-based face recognition, unconstrained video-based face
recognition is still a challenging task due to large volume of data to be
processed and intra/inter-video variations on pose, illumination, occlusion,
scene, blur, video quality, etc. In this work, we consider challenging
scenarios for unconstrained video-based face recognition from multiple-shot
videos and surveillance videos with low-quality frames. To handle these
problems, we propose a robust and efficient system for unconstrained
video-based face recognition, which is composed of modules for face/fiducial
detection, face association, and face recognition. First, we use multi-scale
single-shot face detectors to efficiently localize faces in videos. The
detected faces are then grouped respectively through carefully designed face
association methods, especially for multi-shot videos. Finally, the faces are
recognized by the proposed face matcher based on an unsupervised subspace
learning approach and a subspace-to-subspace similarity metric. Extensive
experiments on challenging video datasets, such as Multiple Biometric Grand
Challenge (MBGC), Face and Ocular Challenge Series (FOCS), IARPA Janus
Surveillance Video Benchmark (IJB-S) for low-quality surveillance videos and
IARPA JANUS Benchmark B (IJB-B) for multiple-shot videos, demonstrate that the
proposed system can accurately detect and associate faces from unconstrained
videos and effectively learn robust and discriminative features for
recognition.
