# Facial Micro-Expression Spotting and Recognition using Time Contrasted Feature with Visual Memory

## TL;DR

This paper introduces a novel joint spatial-temporal neural network architecture that leverages time-contrasted features and a memory module to improve the detection and recognition of facial micro-expressions, achieving superior results on CASMEII.

## Contribution

A new joint spatial-temporal network with time-contrasted features and a memory module for enhanced micro-expression spotting and recognition.

## Key findings

- Outperforms existing methods on CASMEII dataset
- Effectively distinguishes micro-expressions from rapid muscle movements
- Improves accuracy in classifying micro-expression intensity

## Abstract

Facial micro-expressions are sudden involuntary minute muscle movements which reveal true emotions that people try to conceal. Spotting a micro-expression and recognizing it is a major challenge owing to its short duration and intensity. Many works pursued traditional and deep learning based approaches to solve this issue but compromised on learning low-level features and higher accuracy due to unavailability of datasets. This motivated us to propose a novel joint architecture of spatial and temporal network which extracts time-contrasted features from the feature maps to contrast out micro-expression from rapid muscle movements. The usage of time contrasted features greatly improved the spotting of micro-expression from inconspicuous facial movements. Also, we include a memory module to predict the class and intensity of the micro-expression across the temporal frames of the micro-expression clip. Our method achieves superior performance in comparison to other conventional approaches on CASMEII dataset.