Fully Dispersed Haar-like Filters for Enhanced Facial Feature Extraction and Recognition

Document Type : Original Article

Authors
Zeinab Sedaghatjoo
Hossein hosseinzadeh
Ahmad Shirzadi
Department of Mathematics, Persian Gulf University, Bushehr, Iran
10.22034/jcse.2025.548510.1062
Abstract
‎Haar-like filters are well known for their simplicity‎, ‎speed‎, ‎and accuracy in various computer vision tasks‎. ‎This paper proposes a novel algorithm to identify the optimal fully dispersed Haar-like filters for enhanced facial feature extraction‎. ‎Unlike traditional Haar-like filters‎, ‎the proposed filters allow pixels to move freely within an image‎, ‎thereby capturing intricate local features more effectively‎. ‎Extensive experiments on face detection and facial expression recognition demonstrate that the optimized filters can distinguish between face images and clutter with minimal error‎, ‎thereby outperforming existing algorithms‎. ‎By leveraging a dataset-driven approach to optimize filter weights‎, ‎the proposed method achieves high accuracy in facial feature extraction‎, ‎making it a promising tool for various computer vision applications‎. ‎The MATLAB code corresponding to the proposed algorithm is available at https://github.com/Sedaghatjoo/fully-dispersed-Haar-like-filter‎.
Keywords
Feature extraction
Machine learning algorithms
Computer vision
Haar-like filters
Facial expression recognition