  Micro-expressions are hard to spot due to fleeting and involuntary moments of
facial muscles. Interpretation of micro emotions from video clips is a
challenging task. In this paper we propose an affective-motion imaging that
cumulates rapid and short-lived variational information of micro expressions
into a single response. Moreover, we have proposed an
AffectiveNet:affective-motion feature learning network that can perceive subtle
changes and learns the most discriminative dynamic features to describe the
emotion classes. The AffectiveNet holds two blocks: MICRoFeat and MFL block.
MICRoFeat block conserves the scale-invariant features, which allows network to
capture both coarse and tiny edge variations. While MFL block learns
micro-level dynamic variations from two different intermediate convolutional
layers. Effectiveness of the proposed network is tested over four datasets by
using two experimental setups: person independent (PI) and cross dataset (CD)
validation. The experimental results of the proposed network outperforms the
state-of-the-art approaches with significant margin for MER approaches.
