An Evaluation of Machine Learning and Deep Learning for Face Recognition

Abstract

Deep learning, machine learning, and artificial intelligence developments have helped facial recognition technology advance significantly. This technology plays a significant role in various areas, including biometrics, security surveillance, and human-computer interfaces. Compared to standard methods, face recognition systems have superior performance due to the use of deep learning architectures and advanced algorithms. Research and interest in face recognition has increased with its widespread adoption in the residential sector, the healthcare field, as well as in security. Comparative analysis of selected algorithms and research on privacy protection mechanisms shows efforts toward resilience, adaptability, and ethical standards. This study presents AdaBoost and Random Forest as machine learning paradigms that are successful for face recognition algorithms. Two types of deep learning models that contribute to the field, convolutional neural networks (CNN) and long short-term memory networks (LSTM), are also presented. The section on discussion and analysis gives an overview of the works that address different models, applications, as well as objectives used for face recognition research. By pointing out security and privacy, the observations reveal how important the technology is for different domains or sectors, such as national security and education systems. Constant research is essential because of the requirement for ethical reflection, common criteria assessment, and responsible technology. Facial recognition shows the relationship between research and ethics, with study and improvement remaining fundamental as the field continues to evolve.