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[1]徐争元,张 云,黄 磊*.基于单演特征和概率性协作表示的人脸识别[J].绵阳师范学院学报,2018,(05):87-95.[doi:10.16276/j.cnki.cn51-1670/g.2018.05.018]
 XU Zhengyuan,ZHANG Yun,HUANG Lei.Face Recognition Based on Monogenic Features and Probabilistic Collaborative Representation[J].Journal of Mianyang Normal University,2018,(05):87-95.[doi:10.16276/j.cnki.cn51-1670/g.2018.05.018]
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基于单演特征和概率性协作表示的人脸识别(PDF)
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《绵阳师范学院学报》[ISSN:1672-612X/CN:51-1670/G]

卷:
期数:
2018年05期
页码:
87-95
栏目:
计算机与网络技术
出版日期:
2018-05-15

文章信息/Info

Title:
Face Recognition Based on Monogenic Features and Probabilistic Collaborative Representation
文章编号:
1672-612X(2018)05-0087-09
作者:
徐争元 张 云 黄 磊*
皖南医学院医学影像学院,安徽芜湖 241002
Author(s):
XU Zhengyuan ZHANG Yun HUANG Lei
School of Mecidal Image, Wannan Medical College, Wuhu, Anhui 241002
关键词:
人脸识别 概率性协作表示 单演特征
Keywords:
Face recognition Probabilistic collaborative representation monogenic features
分类号:
TP391.9
DOI:
10.16276/j.cnki.cn51-1670/g.2018.05.018
文献标志码:
A
摘要:
针对光照、遮挡、姿态、样本数有限等问题对人脸识别的挑战,提出一种融合单演特征和概率性协作表示的人脸识别方法.概率性协作表示分类方法(ProCRC)相对于稀疏表示分类方法(SRC)和协作表示分类方法(CRC)具有明显的分类优势.但在样本数有限情况下ProCRC方法中样本需更精确地被表示才能获得精确的结果.针对该问题,提出使用单演特征来精确地表示人脸样本.实验结果明表所提出方法能够有效地提高在复杂光照、遮挡、姿态、样本数有限条件下的人脸识别率.
Abstract:
For the challenges of series problems to face recognition, such as light, shade, posture and limited number of samples, this paper proposes a monogenic features based on Probabilistic collaborative representation for face recognition method. Relative to the Sparse representation based classification(SRC)and Collaborative representation based classification(CRC), Probabilistic collaborative representation based on classification(ProCRC)has obvious advantages. But under the condition of limited samples, the samples should be more accurately represented to obtain accurate results in ProCRC method. Aiming at this problem, this paper is proposed to use the monogenic features to accurate representation the face samples. Experimental results show the proposed method effectively improves the recognition rate in the condition of complex light, shade, posture, and limited number of samples.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2017-12-29
基金项目:安徽高校省级自然科学研究重点基金(KJ2017A259); 2016年安徽省省级质量工程项目(2016zy131,2016xnzx037); 2017年芜湖市科技计划项目(2017yf39).
作者简介:徐争元(1986-),男,安徽芜湖人,助教,硕士,研究方向:模式识别、图像处理.
张云(1990-),男,安徽池州人,助教,硕士,研究方向:图像处理.
*通信作者简介:黄磊(1972-),男,安徽芜湖人,副教授,硕士,,研究
更新日期/Last Update: 2018-05-15