[1]张丹丹,李雷. 基于PCANet-RF的人脸检测系统[J].计算机技术与发展,2016,26(02):31-34.
　ZHANG Dan-dan,LI Lei. Face Detection System Based on PCANet-RF[J].,2016,26(02):31-34.
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 基于PCANet-RF的人脸检测系统()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
26
期数:
2016年02期
页码:
31-34
栏目:
智能、算法、系统工程
出版日期:
2016-02-10

文章信息/Info

Title:
 Face Detection System Based on PCANet-RF
文章编号:
1673-629X（2016)02-0031-04
作者:
 张丹丹; 李雷
 南京邮电大学 非机构化数据计算理论与应用研究中心
Author(s):
 ZHANG Dan-dan; LI Lei
关键词:
 ; 人脸检测; 卷积神经网络; 随机森林; 特征提取; 主成分分析网络
Keywords:
 ; face detection; convolutional neural network; random forest; feature extraction; PCANet
分类号:
TP391
文献标志码:
A
摘要:
 文中提出一种基于简化卷积神经网络的特征提取方法的人脸检测算法。图像的特征提取较为复杂，需要大量的预处理。深度学习减少了特征提取的工作量，卷积神经网络就是这方面应用的典型例子。但是，卷积神经网络参数训练时间过长，调参主要依靠实验人员的调参技巧，这大大降低了卷积神经网络应用的初衷。此外，卷积神经网络的分类能力较弱，分类效果并不好。综合以上两点，文中应用一种简化的深度学习方法PCANet(主成分分析网络)提取图像特征，降低对调参的要求，同时用RF(随机森林)对其进行后期分类，提高人脸识别分类效果。实验结果表明，提出的方法对人脸识别率可以达到99%，进一步证明了PCANet在特征提取方面的优越性。
Abstract:
 A face detection system was presented based on a simple convolutional neural network. Feature extraction of image is usually complicated which needs much pretreatment. Deep learning reduces pretreatment,such as convolutional neural network,but it needs more time of training and requires certain ability to adjust the parameters,which contrary to the original intention. What is more,classification capability and result of convolutional neural network is not well. Combination of above,the PCANet for feature extraction is applied to lower the ability to adjust the parameters and Random Forest for image classification is used to improve the recognition rate. This method has got a recognition rate as 99%. Experiments has confirmed that PCANet-RF can be successfully used in image classification.

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更新日期/Last Update: 2016-04-14