Face recognition optimization method based on improved genetic algorithm

A face recognition optimization method based on an improved genetic algorithm comprises the following steps: firstly, performing PCA dimensionality reduction and normalization processing on a sample data set, so that the recognition accuracy is not affected while the operation efficiency is improved; performing global tuning on hyper-parameters of the convolutional neural network model by using an improved genetic algorithm to obtain a convolutional neural network model after hyper-parameter tuning; and finally, inputting the test set into the trained convolutional neural network model to carry out face recognition test. According to the method, the genetic algorithm is combined with the convolutional neural network, the crossover probability and the mutation probability in the genetic algorithm are improved, the situation that a traditional parameter optimization process is prone to falling into a local optimal solution is effectively avoided, and the method has better effects in face recognition accuracy an