  Deep Neural Networks (DNNs) have established themselves as a dominant
technique in machine learning. DNNs have been top performers on a wide variety
of tasks including image classification, speech recognition, and face
recognition. Convolutional neural networks (CNNs) have been used in nearly all
of the top performing methods on the Labeled Faces in the Wild (LFW) dataset.
In this talk and accompanying paper, I attempt to provide a review and summary
of the deep learning techniques used in the state-of-the-art. In addition, I
highlight the need for both larger and more challenging public datasets to
benchmark these systems. The high accuracy (99.63% for FaceNet at the time of
publishing) and utilization of outside data (hundreds of millions of images in
the case of Google's FaceNet) suggest that current face verification benchmarks
such as LFW may not be challenging enough, nor provide enough data, for current
techniques. There exist a variety of organizations with mobile photo sharing
applications that would be capable of releasing a very large scale and highly
diverse dataset of facial images captured on mobile devices. Such an "ImageNet
for Face Recognition" would likely receive a warm welcome from researchers and
practitioners alike.
