  Micro expression recognition (MER)is a very challenging task as the
expression lives very short in nature and demands feature modeling with the
involvement of both spatial and temporal dynamics. Existing MER systems exploit
CNN networks to spot the significant features of minor muscle movements and
subtle changes. However, existing networks fail to establish a relationship
between spatial features of facial appearance and temporal variations of facial
dynamics. Thus, these networks were not able to effectively capture minute
variations and subtle changes in expressive regions. To address these issues,
we introduce an active imaging concept to segregate active changes in
expressive regions of a video into a single frame while preserving facial
appearance information. Moreover, we propose a shallow CNN network: hybrid
local receptive field based augmented learning network (OrigiNet) that
efficiently learns significant features of the micro-expressions in a video. In
this paper, we propose a new refined rectified linear unit (RReLU), which
overcome the problem of vanishing gradient and dying ReLU. RReLU extends the
range of derivatives as compared to existing activation functions. The RReLU
not only injects a nonlinearity but also captures the true edges by imposing
additive and multiplicative property. Furthermore, we present an augmented
feature learning block to improve the learning capabilities of the network by
embedding two parallel fully connected layers. The performance of proposed
OrigiNet is evaluated by conducting leave one subject out experiments on four
comprehensive ME datasets. The experimental results demonstrate that OrigiNet
outperformed state-of-the-art techniques with less computational complexity.
