基于子空间类标传播和正则判别分析的单标记图像人脸识别

Subspace Label Propagation and Regularized Discriminant Analysis Based Single Labeled Image Person Face Recognition

针对单标记图像人脸识别问题,该文提出一种基于子空间类标传播和正则判别分析的半监督维数约简方法。首先,基于子空间假设设计了一种类标传播方法,将类标信息传播到无类标样本上。然后,在传播得到的带类标数据集上使用正则判别分析对数据进行维数约简。最后,在低维空间使用最近邻方法对测试人脸完成识别。在3个公共人脸数据库CMU PIE,Extended Yale B和AR上的实验,验证了该方法的可行性和有效性。

To tackle the problem of single labeled image person face recognition, a subspace label propagation and regularized discriminant analysis based semi-supervised dimensionality reduction method is proposed in this paper. First, a label propagation method based on subspace assumption is designed to propagate the label information from labeled data to unlabeled data. Then, based on the propagated labeled dataset, regularized discriminant analysis is used to conduct dimensionality reduction. Finally, the recognition of testing face is completed in low dimensional space using nearest neighbor classifier. The extensive experiments on three publicly available face databases CMU PIE, Extended Yale B, and AR validate the feasibility and effectiveness of the proposed method.