Modified PCA Algorithm for Face Recognition - MATLAB Implementation

In the following text, I will present a MATLAB code implementation of a modified PCA algorithm for face recognition. PCA is a classic machine learning algorithm widely applied in image recognition tasks. In this specific example, we will utilize the PCA algorithm to recognize human faces. We begin with an original MATLAB code base and implement a series of modifications to ensure proper execution of PCA for face recognition. Key modifications include adjusting input image dimensions and formats, integrating new functions for image preprocessing and data analysis, and optimizing algorithm parameters to enhance recognition accuracy. The implementation involves critical steps such as converting images to grayscale, normalizing pixel values, computing covariance matrices, and performing eigenvalue decomposition to extract principal components. After completing these modifications, we will evaluate our algorithm's performance using a simple test dataset. If the algorithm demonstrates satisfactory performance, we can extend it to recognize larger face datasets or adapt it for other image recognition applications. The code includes functionality for calculating recognition rates, visualizing eigenfaces, and comparing test images with training data using distance metrics. Below is the modified PCA MATLAB code for face recognition, which provides valuable insights and practical applications in the fields of image processing and machine learning. The implementation demonstrates how to handle image datasets, perform dimensionality reduction, and build a basic face recognition system using principal component analysis.