MATLAB Implementation of Face Recognition Algorithm Using BP Neural Network

Face recognition algorithm based on BP neural network represents a classic machine learning approach, where MATLAB implementation effectively validates its recognition performance. The core algorithm employs backpropagation to train multi-layer feedforward neural networks, enabling them to learn and recognize facial features through iterative weight adjustments.

During implementation, facial image preprocessing is essential, involving steps like grayscale conversion, normalization, and feature extraction using MATLAB's image processing toolbox. To enhance training efficiency, a hybrid sampling-full training strategy can be implemented: initially training with subset samples for rapid parameter adjustment using trainlm function, followed by comprehensive training with full dataset using trainbr for Bayesian regularization, balancing time efficiency and model accuracy.

Neural network architecture design is critical, requiring determination of node counts for input, hidden, and output layers. Input layer nodes correspond to facial feature vector dimensions (e.g., 1024 nodes for 32×32 images), while output nodes match target face categories. Hidden layer configuration requires experimental optimization through cross-validation using MATLAB's crossvalind function.

Training parameters require careful configuration: learning rate adjustment via net.trainParam.lr, activation function selection (sigmoid/tanh/ReLU), and overfitting prevention through early stopping or dropout implementation. MATLAB's Neural Network Toolbox simplifies BP network creation (feedforwardnet) and training (train) with built-in optimization algorithms.

Post-training, the model performs classification on new facial images using sim or classify functions. Test set validation through confusion matrix analysis (confusionmat) evaluates recognition accuracy. Performance enhancement can incorporate advanced preprocessing techniques like PCA dimensionality reduction or ensemble methods combining multiple classifiers.