PCA+Fisher: Applying Kernel Functions to Face Recognition Research

This discussion focuses on the application of kernel functions to face recognition research. In this technical approach, PCA (Principal Component Analysis) and Fisher Linear Discriminant Analysis remain among the most extensively utilized and effective methodologies. Both techniques serve as dimensionality reduction methods that enhance computational efficiency while preserving essential information. These approaches are particularly suitable for face recognition applications due to the high-dimensional nature of facial data, where traditional methods often suffer from overfitting issues. The PCA+Fisher combination typically involves first applying PCA for initial dimensionality reduction, followed by Fisher LDA for optimal class separation. This two-stage process significantly improves both recognition accuracy and computational efficiency, making it one of the most prominent techniques in contemporary face recognition systems. Key implementation considerations include kernel function selection (such as polynomial or radial basis functions) and parameter optimization for different dataset characteristics.