Abstract
We proposed a novel approach for face recognition to address the challenging task of recognition using a fusion of nonlinear dimensional reduction; Locally Linear Embedding (LLE) and Principal Component Analysis (PCA). LLE computes a compact representation of high dimensional data combining the major advantages of linear methods, With the advantages of nonlinear approaches which is flexible to learn a broad of class on nonlinear manifolds. The application of LLE, however, is limited due to its lack of a parametric mapping between the observation and the low-dimensional output. In addition, the revealed underlying manifold can only be observed subjectively. To overcome these limitations, we propose our method for recognition by fusion of LLE and Principal Component Analysis (FLLEPCA) and validate their efficiency. Experiments on CMU AMP Face Expression Database and JAFFE databases show the advantages of our proposed novel approach.
| Original language | English |
|---|---|
| Pages (from-to) | 326-333 |
| Number of pages | 8 |
| Journal | Lecture Notes in Computer Science |
| Volume | 3687 |
| Issue number | PART II |
| Publication status | Published - 2005 Nov 4 |
| Event | Third International Conference on Advances in Patten Recognition, ICAPR 2005 - Bath, United Kingdom Duration: 2005 Aug 22 → 2005 Aug 25 |
Fingerprint
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)
Cite this
}
Fusion of locally linear embedding and principal component analysis for face recognition (FLLEPCA). / Abusham, Eimad Eldin; Ngo, David; Teoh, Beng Jin.
In: Lecture Notes in Computer Science, Vol. 3687, No. PART II, 04.11.2005, p. 326-333.Research output: Contribution to journal › Conference article
TY - JOUR
T1 - Fusion of locally linear embedding and principal component analysis for face recognition (FLLEPCA)
AU - Abusham, Eimad Eldin
AU - Ngo, David
AU - Teoh, Beng Jin
PY - 2005/11/4
Y1 - 2005/11/4
N2 - We proposed a novel approach for face recognition to address the challenging task of recognition using a fusion of nonlinear dimensional reduction; Locally Linear Embedding (LLE) and Principal Component Analysis (PCA). LLE computes a compact representation of high dimensional data combining the major advantages of linear methods, With the advantages of nonlinear approaches which is flexible to learn a broad of class on nonlinear manifolds. The application of LLE, however, is limited due to its lack of a parametric mapping between the observation and the low-dimensional output. In addition, the revealed underlying manifold can only be observed subjectively. To overcome these limitations, we propose our method for recognition by fusion of LLE and Principal Component Analysis (FLLEPCA) and validate their efficiency. Experiments on CMU AMP Face Expression Database and JAFFE databases show the advantages of our proposed novel approach.
AB - We proposed a novel approach for face recognition to address the challenging task of recognition using a fusion of nonlinear dimensional reduction; Locally Linear Embedding (LLE) and Principal Component Analysis (PCA). LLE computes a compact representation of high dimensional data combining the major advantages of linear methods, With the advantages of nonlinear approaches which is flexible to learn a broad of class on nonlinear manifolds. The application of LLE, however, is limited due to its lack of a parametric mapping between the observation and the low-dimensional output. In addition, the revealed underlying manifold can only be observed subjectively. To overcome these limitations, we propose our method for recognition by fusion of LLE and Principal Component Analysis (FLLEPCA) and validate their efficiency. Experiments on CMU AMP Face Expression Database and JAFFE databases show the advantages of our proposed novel approach.
UR - http://www.scopus.com/inward/record.url?scp=27244453471&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=27244453471&partnerID=8YFLogxK
M3 - Conference article
AN - SCOPUS:27244453471
VL - 3687
SP - 326
EP - 333
JO - Lecture Notes in Computer Science
JF - Lecture Notes in Computer Science
SN - 0302-9743
IS - PART II
ER -