TY - GEN
T1 - Exploring manifold structure of face images via multiple graphs
AU - Alghamdi, Masheal M.
N1 - KAUST Repository Item: Exported on 2020-10-01
PY - 2013/12/24
Y1 - 2013/12/24
N2 - Geometric structure in the data provides important information for face image recognition and classification tasks. Graph regularized non-negative matrix factorization (GrNMF) performs well in this task. However, it is sensitive to the parameters selection. Wang et al. proposed multiple graph regularized non-negative matrix factorization (MultiGrNMF) to solve the parameter selection problem by testing it on medical images. In this paper, we introduce the MultiGrNMF algorithm in the context of still face Image classification, and conduct a comparative study of NMF, GrNMF, and MultiGrNMF using two well-known face databases. Experimental results show that MultiGrNMF outperforms NMF and GrNMF for most cases.
AB - Geometric structure in the data provides important information for face image recognition and classification tasks. Graph regularized non-negative matrix factorization (GrNMF) performs well in this task. However, it is sensitive to the parameters selection. Wang et al. proposed multiple graph regularized non-negative matrix factorization (MultiGrNMF) to solve the parameter selection problem by testing it on medical images. In this paper, we introduce the MultiGrNMF algorithm in the context of still face Image classification, and conduct a comparative study of NMF, GrNMF, and MultiGrNMF using two well-known face databases. Experimental results show that MultiGrNMF outperforms NMF and GrNMF for most cases.
UR - http://hdl.handle.net/10754/555690
UR - http://proceedings.spiedigitallibrary.org/proceeding.aspx?doi=10.1117/12.2051527
UR - http://www.scopus.com/inward/record.url?scp=84901329222&partnerID=8YFLogxK
U2 - 10.1117/12.2051527
DO - 10.1117/12.2051527
M3 - Conference contribution
SN - 9780819499967
BT - Sixth International Conference on Machine Vision (ICMV 2013)
PB - SPIE-Intl Soc Optical Eng
ER -