MATLAB-Based Face Classification Using AdaBoost Algorithm and JAFFE Database

This MATLAB-based face classification system employs the AdaBoost algorithm and utilizes the JAFFE facial database as its testing library. The system operates by computing similarity metrics between input facial images and all known prototype faces in the database, then sorting these similarities to provide identity information for the input face. Implementation workflow details: 1. First, click the [Train] button to construct the face recognition model. The implementation uses the JAFFE facial database, selecting 15 facial images per individual for training. This approach enhances model accuracy and stability through sufficient training data representation. The AdaBoost algorithm implementation involves creating strong classifiers by combining multiple weak classifiers, typically using Haar-like features for efficient facial feature detection. 2. After training completion, click the [Open] button to select a facial image from the JAFFE database for testing. This step validates whether the model can accurately identify the category of the selected facial image. The code implementation includes image preprocessing routines such as grayscale conversion, normalization, and feature extraction to ensure compatibility with the trained model. 3. Finally, click the [Identify] button to perform recognition on the opened facial image. The system automatically classifies the facial image into its corresponding category and displays the results. The classification process involves comparing extracted features from the input image with stored prototype patterns using similarity measurements like Euclidean distance or cosine similarity, with the highest matching score determining the final identification. Through these implementation steps, the MATLAB-based face recognition system completes both construction and testing phases, effectively providing identity information for input facial images. The system architecture includes modular components for data loading, feature extraction, classifier training, and real-time recognition, ensuring robust performance and accurate results.