基于保持近邻判别嵌入的人脸识别

保持近邻嵌入(NPE)是一种子空间学习方法,具有保持数据流形上局部邻域结构的属性.虽然NPE已在一些领域得到应用,但解决识别任务还有局限性.为改进NPE的识别性能,提出了一种保持近邻判别嵌入(NPDE)人脸识别方法. 在NPDE算法中,有效结合了LDA和NPE的思想,具有很强的判别力,还能根据先验类标签信息保持局部邻域的固有几何关系.在ORL人脸库以及Yale人脸数据库上的实验结果表明提出的方法是有效的.

Face recognition using neighborhood preserving discriminant embedding

Neighborhood preserving embedding (NPE) is a subspace learning algorithm which has the property of preserving local neighborhood structure on the data manifold. Although NPE has been applied in many fields, it has limitations to solve recognition task. To improve the recognition performance of NPE, a new method, called neighborhood preserving discriminant embedding (NPDE), is proposed for face recognition. NPDE effectively combines the ideas of LDA and NPE, i.e. it can hold the strong discriminating power while preserving the intrinsic geometry relations of the local neighborhoods according to prior class-label information. Experimental results on ORL face database and Yale face database demonstrate the effectiveness of the proposed method.