Human Face Recognition Based on Principal Component Analysis and Particle Swarm Optimization-BP Neural Network

This paper proposes an improved face recognition method based on the combination of Principal Component Analysis and Neural Networks. This method adopts Principal Component Analysis (PCA) to abstract principal eigenvectors of the image in order to get best feature description, hence to reduce the number of inputs of neural networks. After this, these image data of reduced dimensions are input into a feed forward neural network to be trained. The weights of neural networks are optimized using Particle Swarm Optimization (PSO) algorithm. Then this well-trained network is tested using samples from standard human face database. The results show that this method gains higher recognition rate in contrast with some other methods.