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.
|Number of pages||9|
|Journal||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Publication status||Published - 2002 Dec 1|
ASJC Scopus subject areas
- Computer Science(all)
- Theoretical Computer Science