基于主成分分析和支持向量机的鲁棒稀疏线性判别分析
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东华理工大学测绘工程学院

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TP391

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国家自然科学基金(42101457、42061077);江西省教育厅科学技术科技(GJJ150591);东华理工大学放射性地质与勘探技术国防重点学科实验室开放基金(REGT1219)


robust sparse linear discriminant analysis method based on principal component analysis and support vector machine
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Faculty of Geomatics East China University of Technology

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    摘要:

    为了提高人脸图像的识别率、识别效率和鲁棒性,提出一种基于主成分分析(Principal Component Analysis, PCA)和支持向量机(support Vector machine,SVM)的鲁棒稀疏线性判别分析方法,通过ORL和YaleB人脸库、COIL20物体库和UCI机器学习库中部分数据集,将本文方法与线性判别分析、鲁棒线性判别分析、基于 范数和巴氏距离的鲁棒线性判别分析、鲁棒自适应线性判别分析和鲁棒稀疏线性判别分析等六种方法进行比较。实验结果表明,在ORL人脸库、COIL20物体库和UCI机器学习库的部分数据集中,在原始图像条件下,本文方法的识别率均值依次为92.80%,97.76%和89.61%,均高于其它5种方法。在YaleB人脸库加入椒盐噪声的条件下,本文方法的识别率均值为81.35%,比其它五种方法高1.37%以上。

    Abstract:

    In order to improve the recognition rate, efficiency and robustness of face images, a robust sparse linear discriminant analysis method based on Principal Component Analysis (PCA) and support vector machine (SVM) is proposed. The proposed method is compared with linear discriminant analysis, robust linear discriminant analysis, robust linear discriminant analysis based on the Bhattacharyya error bound and -norm, robust adaptive linear discriminant analysis and robust sparse linear discriminant analysis through the ORL and YaleB database, COIL20 object database and some datasets in UCI machine learning database. The experimental results show that under the original image condition, the average recognition rate of this method is 92.80%, 97.76% and 89.61% on the ORL database, COIL20 object image database and some datasets in UCI machine learning database, which is higher than the other five methods. Under the condition of adding salt and pepper noise to the YaleB database, the average recognition rate of proposed method is 81.35%, which is more than 1.37% higher than the other five methods.

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鞠厦轶,吕开云,龚循强,等. 基于主成分分析和支持向量机的鲁棒稀疏线性判别分析[J]. 科学技术与工程, , ():

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  • 收稿日期:2021-11-25
  • 最后修改日期:2022-04-06
  • 录用日期:2022-04-30
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