  By the widespread popularity of electronic devices, the emergence of
biometric technology has brought significant convenience to user authentication
compared with the traditional password and mode unlocking. Among many
biological characteristics, the face is a universal and irreplaceable feature
that does not need too much cooperation and can significantly improve the
user's experience at the same time. Face recognition is one of the main
functions of electronic equipment propaganda. Hence it's virtually worth
researching in computer vision. Previous work in this field has focused on two
directions: converting loss function to improve recognition accuracy in
traditional deep convolution neural networks (Resnet); combining the latest
loss function with the lightweight system (MobileNet) to reduce network size at
the minimal expense of accuracy. But none of these has changed the network
structure. With the development of AutoML, neural architecture search (NAS) has
shown excellent performance in the benchmark of image classification. In this
paper, we integrate NAS technology into face recognition to customize a more
suitable network. We quote the framework of neural architecture search which
trains child and controller network alternately. At the same time, we mutate
NAS by incorporating evaluation latency into rewards of reinforcement learning
and utilize policy gradient algorithm to search the architecture automatically
with the most classical cross-entropy loss. The network architectures we
searched out have got state-of-the-art accuracy in the large-scale face
dataset, which achieves 98.77% top-1 in MS-Celeb-1M and 99.89% in LFW with
relatively small network size. To the best of our knowledge, this proposal is
the first attempt to use NAS to solve the problem of Deep Face Recognition and
achieve the best results in this domain.
