Year: 2025
Type: article
Abstract: In biometric security, facial recognition has become a widely adopted contactless alternative to traditional authentication methods such as passwords and fingerprint scanning. This research introduces a novel machine learning-based face recognition system that combines Local Binary Patterns (LBP), Discrete Wavelet Transform (DWT) with Cohen-Daubechies-Feauveau 9/7 filters, and Support Vector Machine (SVM) classifiers. The proposed system operates through three primary stages: image preprocessing, feature extraction, and classification. LBP effectively captures detailed local texture features of the face, while the CDF 9/7 DWT reduces data dimensionality by transforming spatial features into the frequency domain. Finally, SVM is employed to classify the extracted features with high precision. The system is validated using the ORL face dataset comprising 400 images from 40 subjects, achieving a total success rate of 98.22%, outperforming several existing face recognition approaches. Additionally, the model’s robustness is evaluated using False Acceptance Rate (FAR) and False Rejection Rate (FRR) metrics across various thresholds, confirming its reliability. Overall, this integrated approach presents a practical, efficient, and accurate solution suitable for real-time biometric face recognition applications.
DOI: 10.14419/bbjwpr38
Authors: Ojashwini R N, Dr. Raghu N.
Institution: Jain University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4416827445
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