PCA-Based Face Recognition Implementation

In this document, we present a comprehensive implementation of face recognition using Principal Component Analysis (PCA). The program employs eigenvalue decomposition to extract facial features and reduce dimensionality while preserving critical facial patterns. Key components include covariance matrix calculation, eigenface generation, and similarity measurement using Euclidean distance or cosine similarity. The code has been thoroughly tested, but we recommend performing additional validation checks and parameter tuning to ensure optimal performance for your specific dataset. Supplementary materials such as sample datasets and detailed code documentation are provided to facilitate understanding and customization. The implementation features modular functions for data preprocessing, PCA transformation, and classification, allowing easy integration with various machine learning pipelines. Should you encounter any technical issues or have improvement suggestions, please don't hesitate to contact us. We appreciate your interest and look forward to collaborating on further explorations in machine learning and artificial intelligence applications.