AdaBoost-based Face Detection Algorithm

This AdaBoost-based face detection algorithm implements a comprehensive training process and features a modified data loading architecture that significantly reduces memory requirements. The algorithm employs a cascade classification approach where multiple weak classifiers are combined into a strong classifier through iterative boosting. Key implementation aspects include Haar-like feature extraction from input images, where rectangular features are computed efficiently using integral images. The training phase involves weight adjustment for misclassified samples across multiple rounds, with each iteration focusing on previously difficult-to-classify examples. The modified data reading structure utilizes buffered streaming and lazy loading techniques to process training datasets in chunks rather than loading entire datasets into memory simultaneously. This optimization allows handling larger datasets with limited RAM resources. The algorithm demonstrates robust performance in computer vision applications including face recognition systems, emotion detection, and biometric security solutions. Continuous optimization through parameter tuning and feature selection can further enhance detection accuracy and computational efficiency.