Abstract
Non-negative matrix factorization (NMF) is an unsupervised learning algorithm that can extract parts from visual data. The goal of this technique is to find intuitive basis such that training examples can be faithfully reconstructed using linear combination of basis images which are restricted to non-negative values. Thus NMF basis images can be understood as localized features that correspond better with intuitive notions of parts of images. However, there has not been any systematic study to identify suitable distance measure for using NMF basis images for face recognition. In this article we evaluate the performance of 17 distance measures between feature vectors based on the result of the NMF algorithm for face recognition. Recognition experiments are performed using the MIT-CBCL database, CMU AMP Face Expression database and YaleB database.
Original language | English |
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Title of host publication | Computational Intelligence and Security - International Conference, CIS 2006, Revised Selected Papers |
Publisher | Springer Verlag |
Pages | 1039-1049 |
Number of pages | 11 |
ISBN (Print) | 9783540743767 |
DOIs | |
Publication status | Published - 2007 |
Event | 2006 International Conference on Computational Intelligence and Security, CIS 2006 - Guangzhou, China Duration: 3 Nov 2006 → 6 Nov 2006 https://ieeexplore.ieee.org/xpl/conhome/4072023/proceeding https://link.springer.com/book/10.1007/978-3-540-74377-4 |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 4456 LNAI |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 2006 International Conference on Computational Intelligence and Security, CIS 2006 |
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Country/Territory | China |
City | Guangzhou |
Period | 3/11/06 → 6/11/06 |
Internet address |
Scopus Subject Areas
- Theoretical Computer Science
- General Computer Science