[1]曾 坤,姜志侠*.基于多遗传算法的 BP 神经网络人脸识别[J].计算机技术与发展,2021,31(01):77-82.[doi:10. 3969 / j. issn. 1673-629X. 2021. 01. 014]
　ZENG Kun,JIANG Zhi-xia*.Face Recognition Based on BP Neural Network with Multiple Genetic Algorithm[J].,2021,31(01):77-82.[doi:10. 3969 / j. issn. 1673-629X. 2021. 01. 014]
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基于多遗传算法的 BP 神经网络人脸识别()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
31
期数:
2021年01期
页码:
77-82
栏目:
图形与图像
出版日期:
2021-01-10

文章信息/Info

Title:
Face Recognition Based on BP Neural Network with Multiple Genetic Algorithm
文章编号:
1673-629X(2021)01-0077-06
作者:
曾 坤; 姜志侠*
长春理工大学 理学院,吉林 长春 130000
Author(s):
ZENG Kun; JIANG Zhi-xia*
School of Science,Changchun University of Science and Technology,Changchun 130000,China
关键词:
人脸识别; 主成分分析; 独立成分分析; 遗传算法; BP 神经网络
Keywords:
face recognition; principal component analysis; independent component analysis; genetic algorithm; BP neural network
分类号:
TP391. 41
DOI:
10. 3969 / j. issn. 1673-629X. 2021. 01. 014
摘要:
人脸识别应用十分广泛,在实际问题中较高的识别率十分重要,其中 BP 神经网络模型广泛用于人脸识别. 然而在现实应用中,BP 神经网络结构和权值阈值的选取往往依靠经验值,这使得 BP 神经网络存在容易陷入局部最优和收敛速度慢等问题。 针对该问题,提出了一种基于多遗传算法优化 BP 神经网络结构和权值阈值的人脸识别方法。 利用主成分分析算法对人脸图像进行降维,快速独立成分分析算法对人脸图像进行特征提取,以组合算法的方式使得处理后的人脸图像特征更加明显。 通过第一层遗传算法优化 BP 神经网络的结构,第二层遗传算法优化 BP 神经网络的权值阈值,以此解决 BP 神经网络陷入局部最优和收敛速度慢等问题。 基于 ORL 人脸库进行仿真验证,实验结果表明该算法具有较高的识别率。
Abstract:
The application of face recognition is highly extensive,and the high recognition rate is quite important in practical problems.Among them,BP neural network model is widely used in face recognition. However,the selection of BP neural network structure and weights and threshold often depends on empirical value, which makes BP neural network prone to fall into local optimal and slow convergence speed and so on. Aiming at the problem,we propose a face recognition method based on multiple genetic algorithm to optimize the structure and weights and threshold of BP neural network. The principal component analysis is used to reduce the dimension of face image,and the fast independent component analysis is used to extract the feature of face image,so as to make the processed face image more obvious in the way of combination algorithm. The first layer genetic algorithm optimizes the structure and the second optimizes the weights and threshold of BP neural network,in order to solve the problems of falling into local optimization and slow convergence speed and so on. Simulation results based on ORL face database show that the proposed method has high recognition rate.

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更新日期/Last Update: 2020-01-10