Patent ID: 11935326
Assignee: SICHUAN UNIVERSITY
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 7:
8. A face recognition method based on an evolutionary convolutional neural network, comprising:
S1: generating N convolutional neural network structures by an indirect encoding approach according a variable-length encoding algorithm to obtain an initial population, and setting an iteration counter t=1 and a maximum number T of iterations;
S2: training each of first individuals in the initial population, performing a first fitness evaluation using face data, and selecting N parents according to a result of the first fitness evaluation;
S3: performing a crossover on the N parents by using a binary crossover algorithm to obtain N offspring, merging the N parents and the N offspring to form a mixed population, and performing a mutation operation on second individuals in the mixed population;
S4: performing a second fitness evaluation on the second individuals in the mixed population, and applying an environmental selection on the mixed population according to a result of the second fitness evaluation of the mixed population to select N third individuals from the mixed population;
S5: determining whether t is equal to T; when t is equal to T, turning to step S6; when t is not equal to T, using the N third individuals in step S4 as the N parents, incrementing the iteration counter t by one, and returning to step S3; and
S6: selecting an individual network with a first optimal fitness value from the N third individuals, and inputting a face image to be detected into the individual network with the first optimal fitness value to obtain a face recognition result;
wherein step S3 comprises the following sub-steps:
S3.1: successively putting convolutional layers, pooling layers and fully-connected layers into three lists, respectively, according to an arrangement order of network structure layers in a parent of the N parents;
S3.2: obtaining the three lists of each of the N parents according to step S3.1;
S3.3: randomly selecting two parents of the N parents, pairwise matching the two parents with each other according to the convolutional layers, the pooling layers and the fully-connected layers, and aligning heads of two lists matched with each other of the three lists;
S3.4: exchanging the network structure layers at an identical position in the two lists matched with each other to obtain exchanged network structure layers, and putting the exchanged network structure layers into two parents according to an order, wherein the network structure layers are removed out of the two parents in the order;
S3.5: traversing the N parents according to step S3.3 to step S3.4 to obtain the N offspring;
S3.6: merging the N parents and the N offspring to form the mixed population; and
S3.7: performing an adding operation, a deleting operation or a modifying operation on the second individuals in the mixed population to complete the mutation operation on the second individuals in the mixed population.