MATLAB Code Implementation for Face Recognition

In this document, the author discusses face recognition technology. Although this is a brief description, it covers several critical and complex technical components. Face recognition technology identifies individuals by processing and analyzing facial images. The implementation involves PCA (Principal Component Analysis) for feature extraction, which reduces high-dimensional image data to lower dimensions, thereby decreasing computational complexity while preserving essential facial features. In MATLAB, this can be implemented using functions like pca() to compute principal components and transform() to project data onto the new feature space. Following feature extraction, neural networks are employed for classification tasks. MATLAB's Deep Learning Toolbox provides functions such as patternnet() or fitcnet() to create and train neural networks that can effectively classify facial features. Overall, face recognition is a sophisticated technology requiring the integration of multiple techniques—including feature extraction and classification algorithms—to achieve accurate identification of individuals.