AffectiveNet: Affective-Motion Feature Learningfor Micro Expression Recognition
This work addresses the problem of interpreting fleeting micro-expressions from video clips, which is important for applications like lie detection or emotion analysis, but it appears incremental as it builds on existing feature learning methods.
The paper tackled the challenging problem of micro-expression recognition by proposing AffectiveNet, a network that learns discriminative dynamic features from affective-motion imaging, achieving state-of-the-art results with significant margins in person-independent and cross-dataset validations across four datasets.
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.