  Emotion recognition from facial expressions is tremendously useful,
especially when coupled with smart devices and wireless multimedia
applications. However, the inadequate network bandwidth often limits the
spatial resolution of the transmitted video, which will heavily degrade the
recognition reliability. We develop a novel framework to achieve robust emotion
recognition from low bit rate video. While video frames are downsampled at the
encoder side, the decoder is embedded with a deep network model for joint
super-resolution (SR) and recognition. Notably, we propose a novel max-mix
training strategy, leading to a single "One-for-All" model that is remarkably
robust to a vast range of downsampling factors. That makes our framework well
adapted for the varied bandwidths in real transmission scenarios, without
hampering scalability or efficiency. The proposed framework is evaluated on the
AVEC 2016 benchmark, and demonstrates significantly improved stand-alone
recognition performance, as well as rate-distortion (R-D) performance, than
either directly recognizing from LR frames, or separating SR and recognition.
