# Features Extraction Based on an Origami Representation of 3D Landmarks

## TL;DR

This paper introduces a new facial landmarks' representation and preprocessing method that enhance emotion detection accuracy in images and videos, validated on multiple datasets with improved classification metrics.

## Contribution

The paper proposes a novel facial landmarks' representation and preprocessing technique that improve emotion detection accuracy over existing methods.

## Key findings

- Improved facial emotion classification accuracy and F1 score.
- Superiority of the proposed methodology over existing approaches.
- Validated on multiple datasets including CK+.

## Abstract

Feature extraction analysis has been widely investigated during the last decades in computer vision community due to the large range of possible applications. Significant work has been done in order to improve the performance of the emotion detection methods. Classification algorithms have been refined, novel preprocessing techniques have been applied and novel representations from images and videos have been introduced. In this paper, we propose a preprocessing method and a novel facial landmarks' representation aiming to improve the facial emotion detection accuracy. We apply our novel methodology on the extended Cohn-Kanade (CK+) dataset and other datasets for affect classification based on Action Units (AU). The performance evaluation demonstrates an improvement on facial emotion classification (accuracy and F1 score) that indicates the superiority of the proposed methodology.