Sparse Representation-Based Face Recognition Method

This MATLAB implementation demonstrates face recognition based on sparse representation methodology. The sparse representation solution incorporates multiple optimization approaches, including L1 norm minimization (using algorithms like LASSO or basis pursuit), L2 norm minimization (ridge regression techniques), L1-L2 hybrid norms (elastic net regularization), and dictionary learning methods (such as K-SVD or online dictionary learning). Through sparse coding techniques, the system effectively extracts crucial facial features from images and achieves accurate identification. The implementation typically involves creating an overcomplete dictionary from training faces and solving optimization problems to find sparse coefficients that best represent test images. This method also finds applications in other computer vision domains like object detection and image restoration, making sparse representation-based face recognition a promising approach with broad research potential and practical applicability.