MATLAB Implementation of Face Recognition System

In modern society, face recognition technology has become increasingly prevalent. Consequently, the demand for MATLAB-based face recognition systems has grown significantly. This system's primary objective is to identify a specified facial image from a collection of pictures. Using MATLAB, we can implement face recognition through image processing and machine learning algorithms. Specifically, we employ feature extraction methods such as Principal Component Analysis (PCA) or Local Binary Patterns (LBP) to reduce dimensionality and capture distinctive facial characteristics. These features are then processed by classifiers like Support Vector Machines (SVM) or k-Nearest Neighbors (k-NN) to improve recognition accuracy. Key MATLAB functions involved include `pca()` for dimensionality reduction, `fitcsvm()` for SVM training, and `imread()` for image preprocessing. Additionally, system performance can be further enhanced by expanding training datasets and optimizing algorithms through parameter tuning and cross-validation techniques. In summary, implementing a face recognition system in MATLAB is a highly valuable and essential technology applicable to various scenarios such as security systems, facial payment authentication, and access control solutions.