Enhancing Face Recognition through Dimensionality Reduction Techniques and Diverse Classifiers

Authors

  • Aziz Makandar Professor, Dept. of Computer Science, KSAWU, Vijayapura, Karnataka, India.
  • Shilpa Kaman Karnataka State Akkamahadevi Women University Vijayapur
  • Syeda Bibi Javeriya Karnataka State Akkamahadevi Women University Vijayapur

DOI:

https://doi.org/10.5281/zenodo.11109936

Keywords:

Face recognition, PCA, Eigen faces, SVD, LDA, NMF, Random Forest, KNN, SVM, LightGBM

Abstract

Face recognition is essential component of various applications including computer vision, security systems and biometrics. By examining the efficacy of several dimensionality reduction techniques, including Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Singular Value Decomposition (SVD), and Non-Negative Matrix Factorization (NMF), this paper offers a novel approach to face recognition. These techniques are combined with diverse classifiers, including Support Vector Machines (SVM), Random Forest, LightGBM, and k-Nearest Neighbors (KNN), are employed to evaluate their impact on face recognition accuracy. Experiments were conducted on Olivetti faces data set. We have demonstrated the comparative analysis of different dimensionality reduction techniques classifiers in terms of accuracy, precision, recall, f1score. Results shows the potential of integrating PCA with diverse classification models to enhance face recognition accuracy and highlights its applicability in real-world scenarios. 

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Published

2024-06-09

How to Cite

Aziz Makandar, Kaman, S., & Syeda Bibi Javeriya. (2024). Enhancing Face Recognition through Dimensionality Reduction Techniques and Diverse Classifiers. LC International Journal of STEM (ISSN: 2708-7123), 5(1), 36-44. https://doi.org/10.5281/zenodo.11109936