MATLAB Code Implementation for Face Recognition Using Neural Networks

Implementing face recognition functionality in MATLAB enables the construction of an efficient and refined system using neural network technology. Below is the logical framework and key steps for the implementation process: Data Preparation Phase: - Collect facial image datasets and perform preprocessing operations - Convert images into formats suitable for neural network input (e.g., reshaping matrices, normalization) - Split the dataset into training and testing sets using functions like cvpartition or manual indexing Neural Network Construction: - Create feedforward neural networks using the NEWFF function with syntax: net = newff(inputs, targets, hiddenLayers) - Configure network architecture by setting the number of layers and neurons per layer based on complexity requirements - Select appropriate activation functions (e.g., tansig, logsig) and training algorithms (e.g., trainlm for Levenberg-Marquardt) Network Training Phase: - Train the network using the TRAIN function with command: [net, tr] = train(net, inputs, targets) - Set optimal training parameters including learning rate (net.trainParam.lr), maximum epochs (net.trainParam.epochs) - Monitor training progress through performance plots and implement early stopping to prevent overfitting Testing and Simulation: - Test the trained network using the SIM function with: outputs = sim(net, testInputs) - Evaluate performance metrics such as recognition accuracy through confusion matrices (confusionmat) - Optimize network parameters through hyperparameter tuning and cross-validation techniques This approach leverages MATLAB's powerful Neural Network Toolbox, where carefully designed network architectures and parameter configurations can achieve high face recognition accuracy while maintaining code conciseness. For different face recognition application scenarios, adjustments to network depth and training strategies can be made to obtain optimal results through methods like transfer learning or ensemble techniques.