A COMPARATIVE ANALYSIS OF CLASSICAL IMAGE PROCESSING METHODS AND DEEP LEARNING–BASED APPROACHES ON SMALL-SCALE FACE DATASETS
Özet
This study investigates the extent to which classical image processing methods can compete
with existing deep learning approaches on a small-scale human face image dataset. Experiments
were conducted on the ORL face dataset, and classification was performed using Support
Vector Machines (SVM) with Local Binary Patterns (LBP) and Histogram of Oriented
Gradients (HOG) features extracted as part of the classical methods. For comparison, a deep
learning approach was considered, and deep representations obtained using only the
MobileNetV2 model as a feature extractor were classified using SVM. The experimental results
show that the classical HOG-based approach offers high and stable performance (92.5% accuracy) under small data conditions and produces results very close to the deep learningbased reference method. Although the MobileNetV2-based hybrid approach achieved the
highest accuracy (95%), the classical method's significantly lower computational cost and
similar performance without external data dependency are noteworthy for small-scale
applications. The findings reveal that classical image processing methods still offer a valid and
practical alternative in small data scenarios.
Atıf için künye
Sancar, Y. (2025). A COMPARATIVE ANALYSIS OF CLASSICAL IMAGE PROCESSING METHODS AND DEEP LEARNING–BASED APPROACHES ON SMALL-SCALE FACE DATASETS. 5. INTERNATIONAL GAZİANTEP SCIENTIFIC RESEARCH CONGRESS (ss. 417–424).
BibTeX
@inproceedings{sancar2025-76912,
author = {Sancar, Yasin},
title = {A COMPARATIVE ANALYSIS OF CLASSICAL IMAGE PROCESSING METHODS AND DEEP LEARNING–BASED APPROACHES ON SMALL-SCALE FACE DATASETS},
booktitle = {5. INTERNATIONAL GAZİANTEP SCIENTIFIC RESEARCH CONGRESS},
year = {2025},
pages = {417--424},
url = {https://akademik.atauni.edu.tr/yayinlar/76912-a-comparative-analysis-of-classical-image-processing-methods-and-deep-learning}
}