Face recognition using decision fusion of multiple sparse representation-based classifiers

Biao TANG; Wei JIN; Randi FU; Fei GONG

doi:10.11959/j.issn.1000-0801.2018010

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Face recognition using decision fusion of multiple sparse representation-based classifiers

research and development | 更新时间：2024-06-05

- Face recognition using decision fusion of multiple sparse representation-based classifiers
- Telecommunications Science Vol. 34, Issue 4, Pages: 31-40(2018)
- 作者机构：
- 作者简介：
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- DOI：10.11959/j.issn.1000-0801.2018010
  CLC： TP391.4
- Published Online：2018-04，
  
  Published：20 April 2018
- 稿件说明：
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Biao TANG, Wei JIN, Randi FU, et al. Face recognition using decision fusion of multiple sparse representation-based classifiers[J]. Telecommunications science, 2018, 34(4): 31-40.
DOI：

Biao TANG, Wei JIN, Randi FU, et al. Face recognition using decision fusion of multiple sparse representation-based classifiers[J]. Telecommunications science, 2018, 34(4): 31-40. DOI： 10.11959/j.issn.1000-0801.2018010.

摘要

针对目前人脸识别仍然存在顽健性较差的问题，提出一种多稀疏表示分类器决策融合（FR-MSRC）的人脸识别方法。首先提取3组特征，并训练3个稀疏表示子分类器，然后引入决策融合的思想，根据每个子分类器的分类性能，通过迭代运算过程自适应确定各子分类器的融合权值，最后利用融合权值对多个子分类器的输出结果进行决策，实现不同复杂因素干扰下的人脸识别，分别在Yale B、JAFFE和AR人脸库中进行光照、表情、遮挡以及多类型因素混合干扰实验。实验结果表明，本文提出的方法在复杂的环境中仍保持较高的识别率，顽健性更佳。

Abstract

A new approach to face recognition combining decision fusion and multiple sparse representation-based classifiers was proposed to improve the robustness of the traditional methods.Different types of facial features were extracted

followed by training multiple sparse representation sub-classifiers

and then decision fusion was used to obtain the recognition result of the system.The significant advantage of the proposed scheme lines in that the final recognition results were not driven by averaging outputs of multiple sub-classifiers

but driven by combining multiple outputs via weighted fusion method.In particular

the fusion weights were adaptively determined by an iterative pro-cedure according to the different classification performance of each sub-classifier.Extensive experiments on Yale B

JAFFE and AR face databases demonstrate that the proposed approach is much more effective than state-of-the-art methods in dealing with lighting changes

expression changes and face occlusion and multi factor mixed interference.

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references

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