TY - GEN
T1 - Survey of distance measures for NMF-based face recognition
AU - Xue, Yun
AU - TONG, Chong Sze
AU - Zhang, Weipeng
N1 - Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2007
Y1 - 2007
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=38349071129&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-74377-4_109
DO - 10.1007/978-3-540-74377-4_109
M3 - Conference proceeding
AN - SCOPUS:38349071129
SN - 9783540743767
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 1039
EP - 1049
BT - Computational Intelligence and Security - International Conference, CIS 2006, Revised Selected Papers
PB - Springer Verlag
T2 - International Conference on Computational Intelligence and Security, CIS 2006
Y2 - 3 November 2006 through 6 November 2006
ER -