Survey of distance measures for NMF-based face recognition

Yun Xue*, Chong Sze TONG, Weipeng Zhang

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

8 Citations (Scopus)


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 languageEnglish
Title of host publicationComputational Intelligence and Security - International Conference, CIS 2006, Revised Selected Papers
PublisherSpringer Verlag
Number of pages11
ISBN (Print)9783540743767
Publication statusPublished - 2007
EventInternational Conference on Computational Intelligence and Security, CIS 2006 - Guangzhou, China
Duration: 3 Nov 20066 Nov 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4456 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceInternational Conference on Computational Intelligence and Security, CIS 2006

Scopus Subject Areas

  • Theoretical Computer Science
  • Computer Science(all)


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