AffectiveNet: Affective-Motion Feature Learningfor Micro Expression Recognition

TL;DR

This paper introduces AffectiveNet, a deep learning model designed to detect micro-expressions by capturing subtle facial motion features, outperforming existing methods across multiple datasets.

Contribution

The paper proposes AffectiveNet with novel MICRoFeat and MFL blocks for effective micro-expression recognition, capturing scale-invariant and micro-level dynamic features.

Findings

Outperforms state-of-the-art MER methods on four datasets.

Effective in both person-independent and cross-dataset validations.

Captures subtle micro-motions with high discriminative power.

Abstract

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.