基于主成分分析和支持向量机的鲁棒稀疏线性判别分析方法

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。

robust sparse linear discriminant analysis method based on principal component analysis and support vector machine

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.