Method of Combined Feature Enhancement and Network Parameter Optimization for Face Recognition

To solve the problem of the low accuracy of face features extracted by two-dimensional principal component analysis ((2D)²PCA), we introduce an interpolation method for inserting new vectors between feature vectors to improve the display of feature information. To optimize the weights of neural networks, we use a particle swarm optimization algorithm with slowly changing weights. Experimental results show that the combination of these two algorithms can greatly improve the recognition rate.