2DPCA for Face Recognition, Gait Analysis, and Other Image Recognition Applications

2DPCA (Two-Dimensional Principal Component Analysis) is an algorithm designed for image recognition applications such as face recognition and gait analysis. The method initially normalizes irregular images by proportionally adjusting their row and column dimensions, ensuring optimal conditions for training and recognition processes. From an implementation perspective, this typically involves resizing images to a standard resolution while maintaining aspect ratios, followed by covariance matrix computation directly from 2D image matrices without vectorization—a key advantage over traditional PCA. The algorithm is documented with detailed Chinese annotations and instructional prompts, making it accessible for both beginners and professionals to study image recognition principles and applications. Code structure generally includes modules for image preprocessing, eigenvalue decomposition of image covariance matrices, feature projection, and classification using distance metrics like Euclidean or cosine similarity.