An Incremental Face Recognition Algorithm - Incremental PCA Learning Algorithm

This article discusses an incremental face recognition algorithm called the Incremental PCA Learning Algorithm, which enhances recognition accuracy and classification of human faces. Implemented using MATLAB, this algorithm builds upon traditional Principal Component Analysis (PCA) but introduces dynamic learning and data updating capabilities. Unlike standard PCA that requires full dataset retraining, incremental PCA efficiently processes new data samples by updating the eigenbasis without recomputing from scratch. Key implementation aspects include: eigenvector updating using rank-one modifications, covariance matrix incremental updates, and efficient memory management for large datasets. The algorithm maintains a compact representation of feature space while accommodating new facial data, making it particularly suitable for real-time recognition systems and large-scale database applications. We will explore the algorithm's fundamental principles, advantages over batch PCA, implementation methodology using MATLAB's matrix operations and statistical functions, and provide practical examples demonstrating its effectiveness in face recognition scenarios.