Face Recognition Based on Discriminant Sparsity Preserving Embedding
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摘要: 鉴于近年来稀疏表示(Sparse representation,SR)在高维数据例如人脸图像的特征提取与降维领域的快速发展,对原始的稀疏保持投影(Sparsity preserving projection,SPP)算法进行了改进,提出了一种叫做鉴别稀疏保持嵌入(Discriminant sparsity preserving embedding,DSPE)的算法. 通过求解一个最小二乘问题来更新SPP中的稀疏权重并得到一个更能真实反映鉴别信息的鉴别稀疏权重,最后以最优保持这个稀疏权重关系为目标来计算高维数据的低维特征子空间.该算法是一个线性的监督学习算法,通过引入鉴别信息,能够有效地对高维数据进行降维. 在ORL库、Yale库、扩展Yale B库和CMU PIE库上的大量实验结果验证了算法的有效性.Abstract: Motivated by the recent rapid development of sparse representation (SR) on feature extraction and dimensionality reduction of high-dimensional data such as face images, an improved version of sparsity preserving projection (SPP), named discriminant sparsity preserving embedding (DSPE), is proposed in this paper. Through solving a least square problem, the sparse weight of SPP is updated and the discriminant sparse weight which actually reflects the discriminant information is obtained. Then the low-dimensional feature subspace of the original high-dimensional data is evaluated by best preserving such sparse weight relationship. DSPE is a linear supervised learning method which can deal with high-dimensional data effectively by introducing discriminant information. The effectiveness of the proposed method is verified on four popular face databases (ORL, Yale, Extended Yale B, and CMU PIE) with promising results.
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