Exploring Feature Extraction Techniques and SVM for Facial Recognition with Image Generation Using Diffusion Models

Nabila Daly, Faten Khemakhem, Hela Ltifi

2025

Abstract

Facial recognition is a cornerstone of computer vision, with applications spanning security, personalization, and beyond. In this study, we enhance the widely used Labeled Faces in the Wild (LFW) dataset by generating additional images using a diffusion model, enriching its diversity and volume. These augmented datasets were then employed to train Support Vector Machine (SVM) classifiers using three distinct feature extraction methods: Histogram of Oriented Gradients (HOG), Eigenfaces, and Local Binary Patterns (LBP), in combination with SVM (HOG-SVM, Eigenfaces-SVM, and LBP-SVM). Our investigation evaluates the impact of these hybrid approaches on facial recognition accuracy and computational efficiency when applied to the expanded dataset. Experimental results reveal the strengths and limitations of each method, providing valuable insights into the role of feature extraction and data augmentation in improving facial recognition systems.

Paper Citation

in Harvard Style

Daly N., Khemakhem F. and Ltifi H. (2025). Exploring Feature Extraction Techniques and SVM for Facial Recognition with Image Generation Using Diffusion Models. In Proceedings of the 20th International Conference on Evaluation of Novel Approaches to Software Engineering - Volume 1: ENASE; ISBN 978-989-758-742-9, SciTePress, pages 240-251. DOI: 10.5220/0013439900003928

in Bibtex Style

@conference{enase25,
author={Nabila Daly and Faten Khemakhem and Hela Ltifi},
title={Exploring Feature Extraction Techniques and SVM for Facial Recognition with Image Generation Using Diffusion Models},
booktitle={Proceedings of the 20th International Conference on Evaluation of Novel Approaches to Software Engineering - Volume 1: ENASE},
year={2025},
pages={240-251},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013439900003928},
isbn={978-989-758-742-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 20th International Conference on Evaluation of Novel Approaches to Software Engineering - Volume 1: ENASE
TI - Exploring Feature Extraction Techniques and SVM for Facial Recognition with Image Generation Using Diffusion Models
SN - 978-989-758-742-9
AU - Daly N.
AU - Khemakhem F.
AU - Ltifi H.
PY - 2025
SP - 240
EP - 251
DO - 10.5220/0013439900003928
PB - SciTePress