  Deep learning methods have been achieved brilliant results in face
recognition. One of the important tasks to improve the performance is to
collect and label images as many as possible. However, labeling identities and
checking qualities of large image data are difficult task and mistakes cannot
be avoided in processing large data. Previous works have been trying to deal
with the problem only in training domain, however it can cause much serious
problem if the mistakes are in gallery data of face identification. We proposed
gallery data sampling methods which are robust to outliers including wrong
labeled, low quality, and less-informative images and reduce searching time.
The proposed sampling-by-pruning and sampling-by-generating methods
significantly improved face identification performance on our 5.4M web image
dataset of celebrities. The proposed method achieved 0.0975 in terms of FNIR at
FPIR=0.01, while conventional method showed 0.3891. The average number of
feature vectors for each individual gallery was reduced to 17.1 from 115.9 and
it can provide much faster search. We also made experiments on public datasets
and our method achieved 0.1314 and 0.0668 FNIRs at FPIR=0.01 on the
CASIA-WebFace and MS1MV2, while the convectional method did 0.5446, and 0.1327,
respectively.
