Abstract
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.
Original language | English |
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Title of host publication | 2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005 |
Pages | 4870-4875 |
Number of pages | 6 |
Publication status | Published - 12 Dec 2005 |
Event | International Conference on Machine Learning and Cybernetics, ICMLC 2005 - Guangzhou, China Duration: 18 Aug 2005 → 21 Aug 2005 |
Conference
Conference | International Conference on Machine Learning and Cybernetics, ICMLC 2005 |
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Country/Territory | China |
City | Guangzhou |
Period | 18/08/05 → 21/08/05 |
Keywords
- 2DPCA
- Assemble Matrix Metric
- Face Recognition
- Image Recognition
- Palmprint Recognition
ASJC Scopus subject areas
- General Engineering