A Deep Image Compression Framework for Face Recognition

TL;DR

This paper introduces a deep autoencoder-based face image compression method optimized for face recognition, achieving higher verification accuracy on LFW dataset compared to JPEG and JPEG2000.

Contribution

A novel deep convolutional autoencoder framework that jointly optimizes face image compression and recognition for improved accuracy.

Findings

Higher face verification accuracy on LFW after joint training.

Outperforms JPEG and JPEG2000 in face recognition tasks.

Effective compression preserving recognition features.

Abstract

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.