  Face recognition technology has advanced rapidly and has been widely used in
various applications. Due to the extremely huge amount of data of face images
and the large computing resources required correspondingly in large-scale face
recognition tasks, there is a requirement for a face image compression approach
that is highly suitable for face recognition tasks. In this paper, we propose a
deep convolutional autoencoder compression network for face recognition tasks.
In the compression process, deep features are extracted from the original image
by the convolutional neural networks to produce a compact representation of the
original image, which is then encoded and saved by existing codec such as PNG.
This compact representation is utilized by the reconstruction network to
generate a reconstructed image of the original one. In order to improve the
face recognition accuracy when the compression framework is used in a face
recognition system, we combine this compression framework with a existing face
recognition network for joint optimization. We test the proposed scheme and
find that after joint training, the Labeled Faces in the Wild (LFW) dataset
compressed by our compression framework has higher face verification accuracy
than that compressed by JPEG2000, and is much higher than that compressed by
JPEG.
