Face Recognition Using Kernel Linear Discriminant

In this project, we’ll explore the use of kernel linear discriminant (KLD) for face recognition. KLD is a powerful technique for dimensionality reduction and feature extraction, which can be used to improve the accuracy and efficiency of face recognition systems. We’ll start by loading the dataset and preprocessing the images, followed by splitting the dataset into training and test sets. We’ll then use one-hot encoding to prepare the target labels and train a KLD model using the kernel ridge regression algorithm. Finally, we’ll evaluate the performance of our model using a confusion matrix and visualize the results on random test samples. By the end of this project, you’ll have a better understanding of how KLD can be used for face recognition and the steps involved in building a face recognition system.