# Spotting Micro-Expressions on Long Videos Sequences

## TL;DR

This paper introduces baseline results for micro-expression spotting in long videos using local temporal patterns and PCA, evaluated on SAMM and CAS(ME)2 datasets, with a focus on defining true positives and F1-score metrics.

## Contribution

It presents a novel baseline approach for micro-expression spotting using local temporal patterns and PCA, setting evaluation criteria and metrics for future research.

## Key findings

- Baseline F1-score of 0.0316 on SAMM
- Baseline F1-score of 0.0179 on CAS(ME)2
- Evaluation framework with true positive criteria and F1-score metric

## Abstract

This paper presents baseline results for the first Micro-Expression Spotting Challenge 2019 by evaluating local temporal pattern (LTP) on SAMM and CAS(ME)2. The proposed LTP patterns are extracted by applying PCA in a temporal window on several facial local regions. The micro-expression sequences are then spotted by a local classification of LTP and a global fusion. The performance is evaluated by Leave-One-Subject-Out cross validation. Furthermore, we define the criteria of determining true positives in one video by overlap rate and set the metric F1-score for spotting performance of the whole database. The F1-score of baseline results for SAMM and CAS(ME)2 are 0.0316 and 0.0179, respectively.