# Enhanced Facial Feature Extraction and Recignation Using Optimal Fully Dispersed Haar-like Filters

arXiv: https://arxiv.org/abs/2404.10476 · HTML: https://mlsanity.com/paper/2404.10476/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 60 references that are in the map.
- First posted on arXiv 16 Apr 2024.

## Builds on (most cited first, 6 of 6)

- [Age Progression/Regression by Conditional Adversarial Autoencoder](https://mlsanity.com/paper/1702.08423/) (2017)
- [Deep Facial Expression Recognition: A Survey](https://mlsanity.com/paper/1804.08348/) (2018)
- [My Face My Choice: Privacy Enhancing Deepfakes for Social Media Anonymization](https://mlsanity.com/paper/2211.01361/) (2022)
- [Critically examining the Domain Generalizability of Facial Expression Recognition models](https://mlsanity.com/paper/2106.15453/) (2021)
- [A Survey on Computer Vision based Human Analysis in the COVID-19 Era](https://mlsanity.com/paper/2211.03705/) (2022)
- [Object recognition and tracking using Haar-like Features Cascade Classifiers: Application to a quad-rotor UAV](https://mlsanity.com/paper/1903.03947/) (2019)

## Abstract (arXiv)

Haar-like filters are renowned for their simplicity, speed, and accuracy in various computer vision tasks. This paper proposes a novel algorithm to identify optimal fully dispersed Haar-like filters for enhanced facial feature extraction and recognation. Unlike traditional Haar-like filters, these novel filters allow pixels to move freely within images, enabling more effictive capture of intricate local features...