1 research outputs found
Generative One-Shot Face Recognition
One-shot face recognition measures the ability to identify persons with only
seeing them at one glance, and is a hallmark of human visual intelligence. It
is challenging for conventional machine learning approaches to mimic this way,
since limited data are hard to effectively represent the data variance. The
goal of one-shot face recognition is to learn a large-scale face recognizer,
which is capable to fight off the data imbalance challenge. In this paper, we
propose a novel generative adversarial one-shot face recognizer, attempting to
synthesize meaningful data for one-shot classes by adapting the data variances
from other normal classes. Specifically, we target at building a more effective
general face classifier for both normal persons and one-shot persons.
Technically, we design a new loss function by formulating knowledge transfer
generator and a general classifier into a unified framework. Such a two-player
minimax optimization can guide the generation of more effective data, which
effectively promote the underrepresented classes in the learned model and lead
to a remarkable improvement in face recognition performance. We evaluate our
proposed model on the MS-Celeb-1M one-shot learning benchmark task, where we
could recognize 94.98% of the test images at the precision of 99% for the
one-shot classes, keeping an overall Top1 accuracy at for the normal
classes. To the best of our knowledge, this is the best performance among all
the published methods using this benchmark task with the same setup, including
all the participants in the recent MS-Celeb-1M challenge at ICCV
2017\footnote{http://www.msceleb.org/challenge2/2017}