MATLAB Source Code Implementation for Face Recognition

Face recognition implementation in MATLAB can be accomplished using the Neural Network Toolbox combined with image processing techniques. Here is a typical implementation approach: First, dataset preparation is required. Typically, databases containing multiple facial samples (such as ORL or Yale face databases) are used. Images undergo grayscale conversion and are resized to uniform dimensions for consistent analysis. In MATLAB code, this preprocessing can be implemented using functions like rgb2gray() for color conversion and imresize() for standardization. Next comes the feature extraction phase. Methods like Principal Component Analysis (PCA) or Local Binary Patterns (LBP) can be employed to reduce high-dimensional facial image data to key features, thereby reducing computational complexity and improving recognition efficiency. The PCA implementation in MATLAB can utilize the pca() function, while LBP features can be extracted using specialized functions from the Image Processing Toolbox. Subsequently, MATLAB's Neural Network Toolbox is used to construct a classification model. Common network architectures include Multi-Layer Perceptron (MLP) or Convolutional Neural Networks (CNN). During network training, extracted feature vectors are inputted, with appropriate loss functions and optimization algorithms (like backpropagation) configured to adjust network weights. The code typically involves creating a network with patternnet() for MLP or designing CNN layers using convolution2dLayer() and fullyConnectedLayer() functions. Finally, in the testing phase, new facial images undergo identical preprocessing and feature extraction before being fed into the trained neural network for classification, outputting recognition results. The classification can be implemented using the net() function for prediction and comparing outputs with ground truth labels. This method is not only suitable for simple identity verification but can also be extended to more complex scenarios like facial expression recognition or real-time detection. Using MATLAB's Neural Network Toolbox enables rapid model building and optimization, making it suitable for beginners and engineering application development. Key advantages include built-in visualization tools like plotperform() for training monitoring and pretrained models available through Deep Learning Toolbox for transfer learning approaches.