中文摘要：

鉴于人为选取近邻大小和权重矩阵对局部保持投影（LPP）算法的高维人脸图像特征提取有较大影响,结合稀疏表示原理提出了一种稀疏相似保持（SSP）算法。SSP算法利用稀疏表示,在全局结构中自适应地选取数据间的相似关系,构建非负稀疏关系图,在低维空间中保持高维原始数据的内在稀疏特性不变,能有效地提取出低维鉴别特征。在Extend Yale B、CMU PIE人脸数据库上进行实验,其识别率分别达到了87.35%、90.09%,验证了算法的有效性。

英文摘要：

The Locality Preserving Projection（LPP） algorithms have been extensively applied for feature extraction of high dimensional face images, but selecting the neighborhood size and defining the affinity weight have a significant impact on the efficiency of LPP algorithms. In this paper, a new sparse manifold learning method was proposed, called Sparse Similarity Preserving（SSP）, for dimensionality reduction of face images. It adaptively selected the similarity relation in the global structure of the data and constructed non-negative sparse graph using the sparse coefficients which reserved the global sparsity and non-linear manifold structure of face images, effectively extracting the low dimensional discriminant features. Experiments on two popular face databases（Extended Yale B, and CMU PIE）, whose recognition rate reached 87.35% and 90.09%, demonstrated the effectiveness of the presented SSP algorithm.