首页 | 本学科首页   官方微博 | 高级检索  
     

基于TPLBP/HOG特征与DBN深度模型的人脸识别研究
引用本文:陈鹏展,章新志.基于TPLBP/HOG特征与DBN深度模型的人脸识别研究[J].测控技术,2019,38(6):5-10.
作者姓名:陈鹏展  章新志
作者单位:华东交通大学电气与自动化工程学院,江西南昌,330013;华东交通大学电气与自动化工程学院,江西南昌,330013
基金项目:国家自然科学基金项目(61663011);江西省博士后科研择优项目(2015KY19)
摘    要:针对传统人脸识别方法所提取的人脸信息特征较为单一,且分类算法存在局限性的问题,在多特征信息融合的基础上结合深度信念网络(DBN)对人脸进行深度训练并进行识别。首先采取对比度受限自适应均衡化对人脸图像进行预处理,从而削弱光照对人脸识别的影响;然后,将提取到的人脸图像的TPLBP纹理特征和HOG结构特征进行特征融合,得到信息互补的融合特征;最后,将降维后的融合特征作为DBN的输入,通过对DBN深度模型的参数的动态搜索确定最佳值后,基于训练好的深度信念网络实现人脸图像样本的识别。以ORL、AR和Yale-B人脸数据库为基础进行试验,试验结果表明本文方法相较于传统的SVM、KNN和DBN算法准确率有很大提高,鲁棒性强。

关 键 词:深度信念网络  TPLBP纹理  HOG  PCA  RBM

Face Recognition Based on TPLBP/HOG Features and DBN Depth Model
CHEN Peng-zhan,ZHANG Xin-zhi.Face Recognition Based on TPLBP/HOG Features and DBN Depth Model[J].Measurement & Control Technology,2019,38(6):5-10.
Authors:CHEN Peng-zhan  ZHANG Xin-zhi
Affiliation:(School of Electrical and Automation Engineering,East China Jiaotong University,Nanchang 330013,China)
Abstract:The features of face information extracted by traditional face recognition methods are relatively single,and the classification algorithm has limitations.Combined with the deep belief network(DBN),the face is deeply trained and recognized based on multi-feature information fusion.Firstly,the face image is adaptively equalized,thereby the influence of illumination on face recognition is weakened.Then,TPLBP and HOG features that are robust to light,rotation are extracted and their feature information is effectively fused.Finally,based on multi-feature information after dimensionality reduction,a face recognition algorithm based on DBN was proposed after dynamically searching DBN depth model parameters to determine the optimal value.Experiments were conducted on the basis of ORL,AR,Yale-B face database.The experimental results show that compared with the traditional SVM,KNN and DBN algorithms,the accuracy of the proposed method is greatly improved,and the robustness is strong.
Keywords:deep belief network  TPLBP texture  HOG  PCA  RBM
本文献已被 维普 万方数据 等数据库收录!
点击此处可从《测控技术》浏览原始摘要信息
点击此处可从《测控技术》下载全文
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号