Hybrid ResNet50, Convolution Neural Network and Support Vector Machine for Face Recognition in Unconstrained Environment

Authors

Samwel Muchina, Dedan Kimathi University of Technology, Kenya

Juliet Moso, Dedan Kimathi University of Technology, Kenya

Gabriel Kamau, Dedan Kimathi University of Technology, Kenya

Published in

IST-Africa 2026 Conference Proceedings

ISSN: 2576-8581

ISBN: 978-1-905824-76-2

DOI: https://doi.org/10.67725/IST-Africa.2026.JGPP9780

Publisher

IST-Africa Institute and IIMC International Information Management Corporation Ltd

Published in Ireland

Abstract

Face recognition in unconstrained environment poses a real challenge to researchers due to variation in illumination, poses and occlusion. Many studies have been done in face recognition and achieved impressive performance in a controlled environment. When those models are exposed to uncontrolled environment, their performance reduce significantly. Our study introduces a hybrid model of ResNet50 and Support Vector Machine (SVM) for face recognition in uncontrolled environment. The model employed Histogram of Oriented Gradients (HOG) for face detection, ResNet50 and Convolution Neural Network for global and local feature extraction respectively, Principal Component analysis (PCA) for feature reduction, Synthetic Minority Over-sampling Technique (SMOTE) for class balancing and SVM for classification. The model recorded an impressive accuracy of 98.0%, Precision of 97.7%, Recall of 96.6% and F1-Score of 97.2% on LFW dataset and accuracy of 96.3% on CASIA dataset. This result is significant to the real-world face recognition as the environment is uncontrolled and varying conditions can occur at once.

Keywords

Face Recognition, Unconstrained Environment, ResNet50, Support Vector Machine

Cite this paper

S. Muchina, J. Moso and G. Kamau (2026) "Hybrid ResNet50, Convolution Neural Network and Support Vector Machine for Face Recognition in Unconstrained Environment", IST-Africa 2026 Conference Proceedings, Miriam Cunningham and Paul Cunningham (Eds), IST-Africa Institute and IIMC, 2026, ISSN: 2576-8581, ISBN: 978-1-905824-76-2, https://doi.org/10.67725/IST-Africa.2026.JGPP9780

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