Abstract
Graph Embedding (GE) along with its linearization outperforms the traditional linear dimension reduction techniques in face recognition, but there is still room for improvement on GE. This paper proposes an eigenvector weighting technique for a realization of linear GE, namely Neighbourhood Preserving Embedding (NPE) in face verification. The proposed method is called Eigenvector Weighting Function - NPE (EWF-NPE). The eigenspace is decomposed into three subspaces: (1) a subspace that is attributed to facial intra-class variations, (2) a subspace comprises of intrinsic facial characteristics, and (3) a subspace that is attributed to sensor and other external noises. Eigenfeatures are weighted differently in these subspaces. The proposed EWF-NPE ensures that only stable face subspace which yields informative data is emphasized, while the other two noise subspaces are deemphasized. Experimental investigations on FRGC and FERET databases demonstrate promising results of the proposed method.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 2010 5th IEEE Conference on Industrial Electronics and Applications, ICIEA 2010 |
| Pages | 295-300 |
| Number of pages | 6 |
| DOIs | |
| Publication status | Published - 2010 Sep 1 |
| Event | 5th IEEE Conference on Industrial Electronics and Applications, ICIEA 2010 - Taichung, Taiwan, Province of China Duration: 2010 Jun 15 → 2010 Jun 17 |
Other
| Other | 5th IEEE Conference on Industrial Electronics and Applications, ICIEA 2010 |
|---|---|
| Country | Taiwan, Province of China |
| City | Taichung |
| Period | 10/6/15 → 10/6/17 |
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All Science Journal Classification (ASJC) codes
- Electrical and Electronic Engineering
Cite this
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Weighted neighbourhood preserving embedding in face recognition. / Teoh, Andrew Beng Jin; Han, Pang Ying; Siong, Lim Heng.
Proceedings of the 2010 5th IEEE Conference on Industrial Electronics and Applications, ICIEA 2010. 2010. p. 295-300 5516836.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Weighted neighbourhood preserving embedding in face recognition
AU - Teoh, Andrew Beng Jin
AU - Han, Pang Ying
AU - Siong, Lim Heng
PY - 2010/9/1
Y1 - 2010/9/1
N2 - Graph Embedding (GE) along with its linearization outperforms the traditional linear dimension reduction techniques in face recognition, but there is still room for improvement on GE. This paper proposes an eigenvector weighting technique for a realization of linear GE, namely Neighbourhood Preserving Embedding (NPE) in face verification. The proposed method is called Eigenvector Weighting Function - NPE (EWF-NPE). The eigenspace is decomposed into three subspaces: (1) a subspace that is attributed to facial intra-class variations, (2) a subspace comprises of intrinsic facial characteristics, and (3) a subspace that is attributed to sensor and other external noises. Eigenfeatures are weighted differently in these subspaces. The proposed EWF-NPE ensures that only stable face subspace which yields informative data is emphasized, while the other two noise subspaces are deemphasized. Experimental investigations on FRGC and FERET databases demonstrate promising results of the proposed method.
AB - Graph Embedding (GE) along with its linearization outperforms the traditional linear dimension reduction techniques in face recognition, but there is still room for improvement on GE. This paper proposes an eigenvector weighting technique for a realization of linear GE, namely Neighbourhood Preserving Embedding (NPE) in face verification. The proposed method is called Eigenvector Weighting Function - NPE (EWF-NPE). The eigenspace is decomposed into three subspaces: (1) a subspace that is attributed to facial intra-class variations, (2) a subspace comprises of intrinsic facial characteristics, and (3) a subspace that is attributed to sensor and other external noises. Eigenfeatures are weighted differently in these subspaces. The proposed EWF-NPE ensures that only stable face subspace which yields informative data is emphasized, while the other two noise subspaces are deemphasized. Experimental investigations on FRGC and FERET databases demonstrate promising results of the proposed method.
UR - http://www.scopus.com/inward/record.url?scp=77956050018&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=77956050018&partnerID=8YFLogxK
U2 - 10.1109/ICIEA.2010.5516836
DO - 10.1109/ICIEA.2010.5516836
M3 - Conference contribution
SN - 9781424450466
SP - 295
EP - 300
BT - Proceedings of the 2010 5th IEEE Conference on Industrial Electronics and Applications, ICIEA 2010
ER -