  Since the renaissance of deep learning (DL), facial expression recognition
(FER) has received a lot of interest, with continual improvement in the
performance. Hand-in-hand with performance, new challenges have come up. Modern
FER systems deal with face images captured under uncontrolled conditions (also
called in-the-wild scenario) including occlusions and pose variations. They
successfully handle such conditions using deep networks that come with various
components like transfer learning, attention mechanism and local-global context
extractor. However, these deep networks are highly complex with large number of
parameters, making them unfit to be deployed in real scenarios. Is it possible
to build a light-weight network that can still show significantly good
performance on FER under in-the-wild scenario? In this work, we methodically
build such a network and call it as Imponderous Net. We leverage on the
aforementioned components of deep networks for FER, and analyse, carefully
choose and fit them to arrive at Imponderous Net. Our Imponderous Net is a low
calorie net with only 1.45M parameters, which is almost 50x less than that of a
state-of-the-art (SOTA) architecture. Further, during inference, it can process
at the real time rate of 40 frames per second (fps) in an intel-i7 cpu. Though
it is low calorie, it is still power packed in its performance, overpowering
other light-weight architectures and even few high capacity architectures.
Specifically, Imponderous Net reports 87.09\%, 88.17\% and 62.06\% accuracies
on in-the-wild datasets RAFDB, FERPlus and AffectNet respectively. It also
exhibits superior robustness under occlusions and pose variations in comparison
to other light-weight architectures from the literature.
