Genetic Algorithms Pca Face Recognition Algorithm

In recent years, with the artificial intelligence and the image processing technology development, human face graphic information processing and the recognition technology are also renewing day by day. But, at present, many questions are not still to be able to solve, the pending conducted the thorough research.Take the principal component analysis algorithm as the example, in the algorithm operation process, the correlation data component relevance are eliminated. In this algorithm characteristic values are arranged from big to small in turn, and the front great characteristic value and the characteristic vector are retained, and a main component corresponding characteristic space is structured in order to human's face characteristic classification. In this algorithm, great characteristic value corresponding characteristic vector mainly manifests human's face picture overall tendency low frequency component; But the small characteristic value corresponding characteristic vector manifests human's face picture detail information high frequency component. Therefore, this algorithm has obtained human's face picture overall characteristic information, and in human's face picture performance is human's face outline and the gray scale information. Therefore this algorithm has discarded the majority of human's face detail information.This article through"the traditional PCA characteristic space determination", how is the characteristic space determined in the traditional PCA method are discussed; through the experiment discussed"the characteristic space and the recognition rate relations", and demonstrates the characteristic space and the recognition rate relations, simultaneously has proved the genetic algorithm in PCA the application possibility; Introduced the union method in"the genetic algorithm combined with the PCA person face recognition methods", and has discussed the application situation through the experiment. This article mainly using the gene algorithm further improved the principal element to analyze human's face recognition algorithm. Genetic algorithm can be applied to optimal selected the feature space of PCA face recognition method, We can use genetic algorithms to improve it. Three improvements: the first is the improvement of genetic algorithm coding bits, the original N-bit if it is, and now only need N-1 position, and can achieve the same effect, reducing the time complexity and space complexity. Secondly, the initial population of the predecessors is randomly determined; the author is based on the distribution of eigenvalue and eigenvector to determine the non-random initial population method. Finally, we save the maximum fitness for each generation of all chromosomes in the process of genetic algorithm; we optimal select eigenvalue and eigenvector according to variety ways after run of the algorithm.