[1]钱剑滨,陈秀宏.自适应多阶段线性重构表示分类的人脸识别[J].智能系统学报,2020,15(5):964-971.[doi:10.11992/tis.201904002]
 QIAN Jianbin,CHEN Xiuhong.Self-adaptive multi-phase linear reconstruction representation based classification for face recognition[J].CAAI Transactions on Intelligent Systems,2020,15(5):964-971.[doi:10.11992/tis.201904002]
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自适应多阶段线性重构表示分类的人脸识别(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第15卷
期数:
2020年5期
页码:
964-971
栏目:
学术论文—机器感知与模式识别
出版日期:
2020-10-31

文章信息/Info

Title:
Self-adaptive multi-phase linear reconstruction representation based classification for face recognition
作者:
钱剑滨 陈秀宏
江南大学 数字媒体学院,江苏 无锡 214122
Author(s):
QIAN Jianbin CHEN Xiuhong
School of Digital Media, Jiangnan University, Wuxi 214122, China
关键词:
人脸识别自适应多阶段线性重构表示系数分类方法稀疏表示协同表示模式识别
Keywords:
face recognitionself-adaptivemulti-phaselinear reconstructionrepresentation coefficientclassification methodsparse representationcollaborative representationpattern recognition
分类号:
TP391.4
DOI:
10.11992/tis.201904002
文献标志码:
A
摘要:
针对以往基于表示的分类(RBC)方法在类别数较多的数据集上性能不佳的问题,提出了一种自适应多阶段线性重构表示的分类(MPRBC)方法。在每一阶段,首先得到L1范数或L2范数正则化的重构表示系数,然后将表示系数按类求和,根据和的大小来选取相似类,并保留相似类中的全部样本作为下一阶段的训练样本。该策略最终产生具有高分类置信度的稀疏类概率分布,根据类系数的大小自适应选择相似的类,提高了分类计算的效率。实验结果表明,该方法分类性能优于其他RBC方法,特别是在类别数较多的数据集上性能提升明显,并且CPU时间保持相对较低水平。
Abstract:
Representation-based classification (RBC) methods have recently been the promising pattern recognition technologies for object recognition. The representation coefficients of RBC as the linear reconstruction measure can be well used for classifying objects. But RBC methods performs very poorly on large-class-databases and in order to solve the problem of poor performance, a self-adaptive multi-phase linear reconstruction representation based classification (MPRBC) method is proposed. In this process, at first, the reconstruction coefficients regularized by L1-norm or L2-norm are obtained. Then the similar classes are selected according to the sum of the representation coefficients in each class, and all samples of similar classes are retained as training samples for the next stage. This strategy finally produces a sparse class probability distribution with higher classification confidence. The similar classes are selected adaptively according to the values of class coefficients, which improves the efficiency of the classification. Experimental results show that the proposed method is better than other RBC methods, especially on large-class-databases, and CPU time remains relatively low.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2019-04-02。
基金项目:江苏省研究生科研与实践创新计划项目(KYCX18_1871)
作者简介:钱剑滨,硕士研究生,主要研究方向为模式识别、图像处理;陈秀宏,教授,主要研究方向为数字图像处理和模式识别、目标检测与跟踪、优化理论与方法。发表学术论文110余篇
通讯作者:钱剑滨.E-mail:462501979@qq.com
更新日期/Last Update: 2021-01-15