  Surveillance and security scenarios usually require high efficient facial
image compression scheme for face recognition and identification. While either
traditional general image codecs or special facial image compression schemes
only heuristically refine codec separately according to face verification
accuracy metric. We propose a Learning based Facial Image Compression (LFIC)
framework with a novel Regionally Adaptive Pooling (RAP) module whose
parameters can be automatically optimized according to gradient feedback from
an integrated hybrid semantic fidelity metric, including a successfully
exploration to apply Generative Adversarial Network (GAN) as metric directly in
image compression scheme. The experimental results verify the framework's
efficiency by demonstrating performance improvement of 71.41%, 48.28% and
52.67% bitrate saving separately over JPEG2000, WebP and neural network-based
codecs under the same face verification accuracy distortion metric. We also
evaluate LFIC's superior performance gain compared with latest specific facial
image codecs. Visual experiments also show some interesting insight on how LFIC
can automatically capture the information in critical areas based on semantic
distortion metrics for optimized compression, which is quite different from the
heuristic way of optimization in traditional image compression algorithms.
