Selective integrated human face recognition method using genetic algorithm fused with differential evolution

Abstract

The present invention discloses a selective integrated human face recognition method using a genetic algorithm fused with differential evolution. The method comprises: firstly, extracting an HOG feature of a human face image; then, performing dimension reduction on the human face image by using a PCA algorithm to reduce calculation complexity; and finally, performing classified recognition by using data after dimension reduction and applying a GADESEN algorithm. According to the method, with a support vector machine used as a base classifier, N samples are extracted with replacement from an original training set, and according to the method, iteration is performed for T times, and a sample set generated each time is used to train a base classifier model, N generated base classifiers are real-coded to generate an initial population, and the difference vector is used to guide the mutation to produce the high quality individual. The crossover operation uses the parent individual and the mutated individual to produce the cross individual, which increases the individual's diversity, Retention strategy for genetic evolution.

Description

technical field [0001] The present invention belongs to the technical field of machine learning and pattern recognition, uses genetic algorithm fusion differential evolution to select base classifiers, and constructs a selective integrated prediction method with strong generalization ability, in order to achieve accurate prediction of new unknown samples Forecasting purposes. Background technique [0002] In the past few decades, face recognition, as an important research direction of biometric recognition, has received great attention. The research on face recognition has gone through the process of single classifier recognition, integrated classification recognition and deep learning recognition. In the single classifier stage, people are more inclined to optimize the recognition performance of the single classifier and look for a classifier with better performance, but the recognition ability of this single classifier is still difficult to meet the needs of human beings....

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to solve the problems of high computational complexity, high model storage cost and low recognition rate in the existing Bagging integrated face recognition technology, and propose a selective integrated learning method based on genetic algorithm fusion differential evolution (Selective Ensemble Learning Method based on Genetic Algorithmfusion Differential Evolution, GADESEN) applied to face recognition

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