PCA Algorithm in Face Recognition with MATLAB Implementation

This article discusses the MATLAB implementation of the PCA algorithm for face recognition and provides a detailed explanation of the identification process using a nearest neighbor classifier. In the implementation process, we first perform data preprocessing, which includes image denoising and normalization to ensure algorithm accuracy and reliability. The denoising process typically involves filters like median or Gaussian filters to reduce noise, while normalization scales pixel values to a standard range using functions like imresize or mat2gray. Subsequently, we employ the PCA algorithm for feature extraction from preprocessed images, where key MATLAB functions like pca() or eig() are used to compute eigenvectors and reduce dimensionality. The extracted features serve as input to the classifier. Finally, we utilize the nearest neighbor classifier for image classification, implemented through distance calculation functions such as pdist2() or knnsearch(), to determine which person's facial image the input belongs to. Throughout this process, attention must be paid to parameter selection (e.g., number of principal components) and model optimization techniques like cross-validation to ensure recognition accuracy and efficiency.