MATLAB Implementation of Convolutional Neural Networks for Face Recognition

This documentation presents a comprehensive approach to face recognition using Convolutional Neural Networks (CNN) implemented in MATLAB. MATLAB serves as an optimal programming environment for this task due to its powerful image processing capabilities and extensive machine learning工具箱. The implementation leverages MATLAB's Deep Learning Toolbox, which provides pre-built functions for constructing CNN architectures, including convolutional layers (using conv2d), pooling layers (maxPooling2d), and fully connected layers.

The face recognition algorithm follows a systematic workflow: first, image preprocessing techniques such as normalization and data augmentation are applied using imresize and augmentImage functions. The CNN architecture typically consists of multiple convolutional layers for feature extraction, followed by ReLU activation functions (reluLayer) and batch normalization layers (batchNormalizationLayer) to improve training stability. The implementation includes key MATLAB functions like trainNetwork for model training and classify for prediction, with options to customize training parameters through trainingOptions.

By employing CNNs in MATLAB, we achieve enhanced accuracy and performance in face recognition tasks. The code implementation demonstrates how to handle dataset loading with imageDatastore, configure layer architectures using layerGraph, and evaluate model performance with confusion matrices. This approach results in an efficient and reliable face recognition system that can be easily adapted for various real-world applications through MATLAB's intuitive programming interface and visualization tools.