Enhanced CNN-Based Facial Recognition Optimization in MATLAB

Abstract

As we advance further into the digital age, facial recognition technology has become a crucial innovation across various fields, including security authentication, surveillance, and identity verification. This study focuses on enhancing the Convolutional Neural Network (CNN) framework within the MATLAB environment, significantly improving the performance of facial recognition algorithms. The manuscript begins by reviewing the historical development and current achievements in the field of facial recognition. It then delves into the theoretical foundations and key technologies underpinning facial recognition systems. The primary objective of this research is to develop a sophisticated facial recognition algorithm based on CNN, utilizing effective image preprocessing techniques such as grayscale conversion, noise reduction, and feature extraction to markedly enhance recognition accuracy and processing speed. Experiments conducted in MATLAB demonstrate the dual improvements in efficiency and speed provided by the optimized algorithm over traditional methods. Furthermore, the paper examines the algorithm's adaptability in complex scenarios, as well as the challenges and strategies likely to be encountered in practical applications. The findings of this research not only affirm the feasibility of the proposed algorithm but also highlight future directions and methodologies for advancing facial recognition technology.