由于稀疏表示方法在人脸分类算法中的成功使用,在此基础上提出了一种更为有效的基于稀疏表示(SRC)和弹性网络相结合的分类方法.为了加强样本间的协作表示能力以及增强处理强相关性变量数据的能力,基于迭代动态剔除机制,提出一种结合弹性网络的稀疏分解方法.通过采用训练样本的线性组合来表示测试样本,并运用迭代机制从所有样本中剔除对分类贡献度较小的类别和样本,采用Elastic Net算法来进行系数分解,从而选择出对分类贡献度较大的样本和类别,最后根据计算相似度对测试样本进行分类.在ORL、FERET和AR三个数据集进行了许多实验,实验结果显示算法识别率分别达到了98.75％、86.62％、99.72％,表明了所提算法的有效性.所提算法相比LASSO和SRC-GS等方法,在系数分解过程中增强了处理高维小样本和强相关性变量数据的能力,突出了稀疏约束在该算法中的重要性,具有更高的准确性和稳定性,能够更加有效地适用于人脸分类.%Because of the successful use of the sparse representation in face classification algorithm,a more efficient classification method based on Sparse Representation-based pattern Classification (SRC) and elastic network was proposed.To enhance the ability of collaborative representation and enhance the ability to deal with strongly correlated data,a sparse decomposition method based on elastic network was proposed based on the iterative dynamic culling mechanism.Test samples were represented by a linear combination of training samples,and the iterative mechanism was used to remove the categories and samples with less contribution to the classification from all the samples,the Elastic Net algorithm was used for coefficient decomposition to select the samples and classes with high contribution to the classification.Finally,the test samples were classified according to the similarity.The experiment results show that the recognition rate of the algorithm is 98.75％,86.62％ and 99.72％ respectively for the ORL,FERET and AR data sets which shows the effectiveness of the proposed algorithm.Compared with the methods of LASSO and SRC-GS,the proposed algorithm can enhance the ability of dealing with high-dimension small sample and strongly correlated variable data in the process of coefficient decomposition.It highlights the importance of sparse constraint in the algorithm and has higher accuracy and stability,and can be more effectively applied to face classification.
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