Explore MATLAB source code curated for "人脸识别" with clean implementations, documentation, and examples.
Linear Discriminant Analysis (LDA) serves as a fundamental algorithm in face recognition, providing beginners with practical reference implementation and crucial dimensionality reduction techniques
Sparse representation-based face recognition using the ORL database, containing 40 subjects with 10 images each. The implementation randomly selects training samples while using the remaining images as test samples. The final recognition accuracy is calculated as the average of 20 independent trials to ensure statistical reliability.
A ready-to-use PCA face recognition system requiring no debugging. Features efficient feature extraction using eigenface methodology and demonstrates reliable performance. Specifically designed for easy integration and handling of facial variations including lighting conditions and facial expressions.
MATLAB-based face recognition using Haar-like feature training, providing a practical implementation with algorithmic insights for developers.
2DPCA is applied to face recognition, gait analysis, and other image recognition tasks. It first normalizes irregular images by adjusting row and column proportions, then proceeds with training and recognition. The implementation includes comprehensive Chinese annotations and prompts, facilitating learning and reference for developers and researchers.
A MATLAB program for face recognition utilizing the ADABOOST algorithm with detailed code implementation and explanatory notes
Implementation of Fuzzy SVM and ICA Algorithms for Enhanced Face Recognition Systems
A MATLAB program demonstrating the application of wavelet transform combined with principal component analysis in face recognition systems, featuring preprocessing, feature extraction, and classification implementation.
Implementing Face Recognition Systems with Artificial Neural Networks: Architecture Design and Algorithmic Approaches
Face recognition system using 2D-PCA methodology, implemented and tested on the ORL face database with comprehensive code implementation details