Face Recognition Experiment Using Kernel Fisher Discriminant Analysis (KFDA) on ORL Face Database

In this experiment, we conduct face recognition using Kernel Fisher Discriminant Analysis (KFDA) on the ORL face database. The ORL standard face database is a renowned benchmark containing 40 distinct subjects, each with 10 different BMP images, totaling 400 images. This dataset is widely adopted in face recognition research due to its diverse and comprehensive nature, enabling robust evaluation of various face recognition algorithms. We selected this database as our experimental foundation to ensure accurate and reliable results. Through KFDA implementation, we project face images into a high-dimensional feature space using kernel functions (e.g., polynomial or RBF kernels), followed by Fisher discriminant analysis to maximize between-class separation while minimizing within-class variance. The code typically involves computing kernel matrices, solving generalized eigenvalue problems for discriminant vectors, and performing classification using projected features. This approach effectively handles non-linear patterns in face data while maintaining computational efficiency through kernel tricks. We believe this experiment will provide valuable insights and contributions to both research and practical applications in the face recognition domain, particularly in demonstrating how kernel methods enhance traditional linear discriminant analysis for complex pattern recognition tasks.