MATLAB Implementation of Face Recognition Using ADABOOST Method

This article introduces how to perform face recognition using the ADABOOST method, along with a MATLAB program implementation for this process. ADABOOST is a widely-used machine learning algorithm that constructs a strong classifier by combining multiple weak classifiers, thereby enhancing classification accuracy. In face recognition applications, ADABOOST has gained extensive adoption due to its effectiveness in identifying human faces and distinguishing them from non-face images. Our MATLAB program employs this algorithm to detect faces in input images and outputs the recognition results. The implementation includes several key components: feature extraction using Haar-like features, weak classifier training based on threshold decisions, and iterative weight updating during the boosting process. The program structure involves loading training datasets, preprocessing images through normalization, and implementing the core ADABOOST algorithm with adjustable parameters for iteration count and weak classifier selection. We provide comprehensive explanations of the code to help readers better understand both the algorithmic principles and program implementation details. The code includes functions for data loading (load_dataset), feature calculation (compute_haar_features), classifier training (train_weak_classifier), and the main boosting loop (adaboost_training). Each function contains comments explaining its role in the overall face recognition pipeline, with special attention to error calculation and weight redistribution mechanisms that are central to the ADABOOST method.