Assembled matrix distance metric for 2DPCA-based face and palmprint recognition
Wangmeng Zuo, Kuanquan Wang, David Zhang · 2005
Two-dimensional Principal component analysis (2DPCA) is a novel image representation approach recently developed for image recognition. One advantage of 2DPCA is that it can extract feature matrix using a straightforward image projection technique. In this paper, we propose an assembled matrix distance metric (AMD) to measure the distance between two feature matrices. To test the efficiency of the proposed distance measure, we use two image databases, the ORL face and the PolyU palmprint. The experimental results show that the assembled matrix distance metric is very effective in 2DPCA based image recognition.