Development of Face Recognition System Using Support Vector Machine Algorithm

Abstract

This research paper describes a face recognition system constructed with the Support Vector Machine (SVM) method. The technique implements the Convolutional Neural Network (CNN) approach for extracting features, which improves the accuracy of recognising faces. The method confirms an efficient and accurate facial recognition procedure by utilising SVM's ability to detect complex patterns within retrieved characteristics. The article explores how the system is implemented and optimised performance, with a focus on obtaining performance in real time. The experimental findings demonstrate the practical application of the SVM-based facial recognition system, emphasising its numerous potential applications in security, the authentication process, as well as interaction between humans and computers. The implementation of CNN for extracting features provides refinement to the system, proving its flexibility to complex face characteristics and confirming its feasibility in actual applications. The results indicate that the suggested system is an effective facial recognition technique. Furthermore, the system is computationally efficient, taking just a few seconds to evaluate an individual face for extracting features and recognition. The system for identifying faces uses modern approaches to enhance both speed and precision, making it a potential option for a variety of applications. Its capacity to swiftly and correctly detect faces in real-time circumstances is a substantial development in facial identification technologies. The system's capability has been comprehensively evaluated, and it satisfies the essential criteria for practical implementation in real-world situations, making it an important addition to the continued development of secure and efficient face recognition systems.