  Facial expressions vary from the visible to the subtle. In recent years, the
analysis of micro-expressions $-$ a natural occurrence resulting from the
suppression of one's true emotions, has drawn the attention of researchers with
a broad range of potential applications. However, spotting microexpressions in
long videos becomes increasingly challenging when intertwined with normal or
macro-expressions. In this paper, we propose a shallow optical flow
three-stream CNN (SOFTNet) model to predict a score that captures the
likelihood of a frame being in an expression interval. By fashioning the
spotting task as a regression problem, we introduce pseudo-labeling to
facilitate the learning process. We demonstrate the efficacy and efficiency of
the proposed approach on the recent MEGC 2020 benchmark, where state-of-the-art
performance is achieved on CAS(ME)$^{2}$ with equally promising results on SAMM
Long Videos.
