Published Paper Details:

This paper introduces a novel hybrid classifier designed to achieve rapid and efficient face recognition by leveraging Local Binary Pattern (LBP) algorithms. Our proposed approach capitalizes on the strengths of LBP-based feature extraction methods while incorporating different classifiers, such as Support Vector Machine (SVM) and Random Forest (RF), to enhance recognition performance. The process involves extracting LBP features from the input face image, which are then utilized as input to the classifiers. By combining these elements, our hybrid classifier achieves high accuracy in face recognition while significantly reducing computational complexity and processing time compared to traditional LBP-based methods. To validate the effectiveness of our approach, we conducted experiments on publicly available face recognition datasets. The experimental results confirm the superiority of our hybrid classifier, making it a suitable choice for real-time face recognition applications.