Face Recognition Using KPCA and SVM

KPCA and SVM are jointly employed for face recognition applications. SVM significantly improves classification performance through optimized support vector machine algorithms that accurately distinguish between different facial patterns. In implementation, SVM classifiers typically utilize kernel functions like RBF or polynomial kernels to handle non-linear separability, with key parameters including penalty factor C and kernel parameters tuned via cross-validation. KPCA serves as an effective feature extraction method derived from SVM's kernel function concept, which maps original data into higher-dimensional spaces using kernel tricks to better capture facial characteristics. The KPCA algorithm involves computing kernel matrices and performing eigenvalue decomposition to extract principal components in feature space. This combination of KPCA and SVM not only enhances face recognition accuracy but also strengthens feature extraction capabilities. A typical implementation workflow includes: preprocessing facial images, applying KPCA for dimensionality reduction and feature extraction, then feeding the transformed features into SVM for classification training and prediction.