A structure-preserved local matching approach for face recognition
A structure-preserved local matching approach for face recognition
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DOI：
10.1016/j.patrec.2010.11.014
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发表时间：
2011-02
期刊：
影响因子：
--
通讯作者：
Jianzhong Wang;Zhiqiang Ma;Baoxue Zhang;Miao Qi;Jun Kong
中科院分区：
文献类型：
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作者：
Jianzhong Wang;Zhiqiang Ma;Baoxue Zhang;Miao Qi;Jun Kong
本文提出了一种新的局部匹配方法--保结构投影（SPP）方法。与大多数现有的局部匹配方法在特征提取过程中忽略了不同子模式集之间的相互作用，即，它们假设不同的子模式集是独立的; SPP考虑了人脸的整体上下文，并且可以在子空间中保持每个人脸图像的几何结构。此外，在我们的方法中，子模式集的固有流形结构也可以保持。该方法利用SPP对原始人脸图像中的所有子模式进行训练，得到一个统一的子空间，在该子空间中进行识别。在三个标准人脸库（Yale，Extended YaleB和PIE）上的实验证明了该算法的有效性。实验结果表明，SPP优于其他整体和局部匹配方法。
In this paper, a novel local matching method called structure-preserved projections (SPP) is proposed for face recognition. Unlike most existing local matching methods which neglect the interactions of different sub-pattern sets during feature extraction, i.e., they assume different sub-pattern sets are independent; SPP takes the holistic context of the face into account and can preserve the configural structure of each face image in subspace. Moreover, the intrinsic manifold structure of the sub-pattern sets can also be preserved in our method. With SPP, all sub-patterns partitioned from the original face images are trained to obtain a unified subspace, in which recognition can be performed. The efficiency of the proposed algorithm is demonstrated by extensive experiments on three standard face databases (Yale, Extended YaleB and PIE). Experimental results show that SPP outperforms other holistic and local matching methods.