MATLAB Implementation of PCA-Based Face Recognition

This text discusses PCA-based face recognition, a technique utilizing Principal Component Analysis for facial identification. The PCA algorithm reduces high-dimensional facial image data to lower dimensions, extracting the most significant features for accurate face recognition. In MATLAB implementation, key functions like pca() or custom covariance matrix calculations are used to compute eigenvectors (principal components) from the training dataset. The projection of new facial images onto this eigenface subspace allows efficient comparison and identification. PCA face recognition technology has been widely applied in various fields including security surveillance, facial payment systems, and information security. Furthermore, with the development of deep learning technologies, more advanced face recognition algorithms such as convolutional neural network (CNN)-based approaches have emerged. While these algorithms are more complex than PCA-based methods, they offer higher recognition accuracy and superior robustness, garnering increasing attention and research interest. Implementation-wise, CNN-based methods in MATLAB typically involve using Deep Learning Toolbox functions like trainNetwork() with specialized architectures like ResNet or VGG adapted for facial features.