Face Recognition Approach Using the Deep Learning Techniques

Abstract

Facial recognition technology has emerged as a pivotal tool in both security and technical domains. Its adoption has accelerated in several developed nations, where it finds applications across various sectors. For instance, institutions and businesses leverage this technology to monitor employees and detect unauthorized individuals, while banks utilize it to verify identities during cash withdrawals, serving as an alternative to traditional ATM cards. In this context, our research aims to propose a machine learning-based method for improving facial recognition accuracy. We conducted a thorough review of existing algorithms in the field and identified Support Vector Machine (SVM) and Principal Component Analysis (PCA) as the most effective based on prior studies. To evaluate these algorithms, we compared SVM and PCA in the context of identity verification. Our experiments involved training the algorithms on a dataset comprising 5,749 facial images. The results demonstrated that the SVM algorithm significantly outperformed PCA in terms of accuracy. We believe that the successful application of the SVM algorithm can provide substantial benefits to institutions looking to enhance their security measures through reliable facial recognition systems.