Face Feature Extraction Using PCA with SVM-Based Multi-Class Recognition

This application implements Principal Component Analysis (PCA) for facial feature extraction from images. The PCA algorithm reduces high-dimensional facial feature data to lower dimensions through eigenvalue decomposition of the covariance matrix, typically achieved using functions like numpy.linalg.eig() in Python or eig() in MATLAB. This dimensionality reduction enhances classifier accuracy and computational efficiency by concentrating the most significant variance components. Subsequently, we utilize a Support Vector Machine (SVM) multi-class classifier for facial recognition. SVM, grounded in statistical learning theory, constructs optimal hyperplanes in high-dimensional feature spaces using kernel functions (e.g., linear, RBF, or polynomial) to maximize margin separation between classes. The implementation typically involves sklearn.svm.SVC in Python with one-vs-rest or one-vs-one strategies for multi-class classification. Finally, we developed a streamlined graphical user interface (GUI) using frameworks like Tkinter or PyQt, enabling users to easily input facial images and obtain recognition results through integrated PCA feature extraction and SVM classification pipelines.