Abstract
Recently in face recognition, as opposed to our expectation, the performance of an ICA (Independent Component Analysis) method combined with LDA (Linear Discriminant Analysis) was reported as lower than an ICA only based method. This research points out that (ICA+LDA) methods have not got a fair comparison for evaluating its recognition performance. In order to incorporate class specific information into ICA, we have employed FLD (Fisher Linear Discriminant) and have proposed our (ICA+FLD) method. In the experimental results, we report that our (ICA+FLD) method has better performance than ICA only based methods as well as other representative methods such as Eigenface and Fisherface methods.
| Original language | English |
|---|---|
| Pages (from-to) | 10-18 |
| Number of pages | 9 |
| Journal | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
| Volume | 2359 LNCS |
| Publication status | Published - 2002 Dec 1 |
| Externally published | Yes |
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ASJC Scopus subject areas
- Computer Science(all)
- Theoretical Computer Science
Cite this
Face recognition based on ICA combined with FLD. / Yi, Juneho; Kim, Jongsun; Choi, Jongmoo; Han, Junghyun; Lee, Eunseok.
In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 2359 LNCS, 01.12.2002, p. 10-18.Research output: Contribution to journal › Article
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TY - JOUR
T1 - Face recognition based on ICA combined with FLD
AU - Yi, Juneho
AU - Kim, Jongsun
AU - Choi, Jongmoo
AU - Han, Junghyun
AU - Lee, Eunseok
PY - 2002/12/1
Y1 - 2002/12/1
N2 - Recently in face recognition, as opposed to our expectation, the performance of an ICA (Independent Component Analysis) method combined with LDA (Linear Discriminant Analysis) was reported as lower than an ICA only based method. This research points out that (ICA+LDA) methods have not got a fair comparison for evaluating its recognition performance. In order to incorporate class specific information into ICA, we have employed FLD (Fisher Linear Discriminant) and have proposed our (ICA+FLD) method. In the experimental results, we report that our (ICA+FLD) method has better performance than ICA only based methods as well as other representative methods such as Eigenface and Fisherface methods.
AB - Recently in face recognition, as opposed to our expectation, the performance of an ICA (Independent Component Analysis) method combined with LDA (Linear Discriminant Analysis) was reported as lower than an ICA only based method. This research points out that (ICA+LDA) methods have not got a fair comparison for evaluating its recognition performance. In order to incorporate class specific information into ICA, we have employed FLD (Fisher Linear Discriminant) and have proposed our (ICA+FLD) method. In the experimental results, we report that our (ICA+FLD) method has better performance than ICA only based methods as well as other representative methods such as Eigenface and Fisherface methods.
UR - http://www.scopus.com/inward/record.url?scp=57949099774&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=57949099774&partnerID=8YFLogxK
M3 - Article
AN - SCOPUS:57949099774
VL - 2359 LNCS
SP - 10
EP - 18
JO - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
JF - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SN - 0302-9743
ER -