  Automated human emotion recognition from facial expressions is a well-studied
problem and still remains a very challenging task. Some efficient or accurate
deep learning models have been presented in the literature. However, it is
quite difficult to design a model that is both efficient and accurate at the
same time. Moreover, identifying the minute feature variations in facial
regions for both macro and micro-expressions requires expertise in network
design. In this paper, we proposed to search for a highly efficient and robust
neural architecture for both macro and micro-level facial expression
recognition. To the best of our knowledge, this is the first attempt to design
a NAS-based solution for both macro and micro-expression recognition. We
produce lightweight models with a gradient-based architecture search algorithm.
To maintain consistency between macro and micro-expressions, we utilize dynamic
imaging and convert microexpression sequences into a single frame, preserving
the spatiotemporal features in the facial regions. The EmoNAS has evaluated
over 13 datasets (7 macro expression datasets: CK+, DISFA, MUG, ISED, OULU-VIS
CASIA, FER2013, RAF-DB, and 6 micro-expression datasets: CASME-I, CASME-II,
CAS(ME)2, SAMM, SMIC, MEGC2019 challenge). The proposed models outperform the
existing state-of-the-art methods and perform very well in terms of speed and
space complexity.
