  Although face recognition has been improved much as the development of Deep
Neural Networks, SIPP(Single Image Per Person) problem in face recognition has
not been better solved, especially in practical applications where searching
over complicated database. In this paper, a combination of modified mean search
and LSH method would be introduced orderly to improve the precision and recall
of SIPP face recognition without retrain of the DNN model. First, a modified
SVD based augmentation method would be introduced to get more intra-class
variations even for person with only one image. Second, an unique rule based
combination of modified mean search and LSH method was proposed the first time
to help get the most similar personID in a complicated dataset, and some
theoretical explaining followed. Third, we would like to emphasize, no need to
retrain of the DNN model and would easy to be extended without much efforts. We
do some practical testing in competition of Msceleb challenge-2 2017 which was
hold by Microsoft Research, great improvement of coverage from 13.39% to
19.25%, 29.94%, 42.11%, 47.52% at precision 99%(P99) would be shown latter,
coverage reach 94.2% and 100% at precision 97%(P97) and 95%(P95) respectively.
As far as we known, this is the only paper who do not fine-tuning on
competition dataset and ranked top-10. A similar test on CASIA WebFace dataset
also demonstrated the same improvements on both precision and recall.
