Selective Ensemble Face Recognition Method Based on Genetic Algorithm and Differential Evolution

Abstract

The invention discloses a selective integrated face recognition method based on genetic algorithm fusion differential evolution. Firstly, the HOG feature of the face image is extracted, and then the PCA algorithm is used to reduce the dimension of the face image, so as to reduce the computational complexity. Finally, use After dimensionality reduction, the GADESEN algorithm is used to classify and identify the data. This method takes the support vector machine as the base classifier, extracts N samples from the original training set with replacement, iterates T times according to this method, and uses the sample set generated each time to train the base classifier model. The classifier encodes real numbers to generate an initial population. In the mutation operation, the difference vector is used to guide the mutation and then produce high-quality individuals. The crossover operation uses the parent individual and the mutant individual to generate crossover individuals, which increases the diversity of individuals. Retention strategies 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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