Chan, Lih-Heng and Salleh, Sh-Hussain and Tin, Chee-Ming (2009) PCA, LDA and neural network for face identification. In: 2009 4th IEEE Conference on Industrial Electronics and Applications, ICIEA 2009. Institute of Electrical and Electronics Engineers, New York, 1256 -1259. ISBN 978-142442800-7
Full text not available from this repository.
Official URL: http://dx.doi.org/10.1109/ICIEA.2009.5138403
Algorithms based on Principal Component Analysis (PCA) and subspace Linear Discriminant Analysis (LDA) are popular in face recognition. PCA is used to perform dimension reduction on human face data and LDA creates another subspace to improve discriminant of PCA features. In this paper, we propose Artificial Neural Networks (ANN) as an alternative to replace Euclidean distances in classification of human face features extracted by PCA and LDA. ANN is well recognized by its robustness and good learning ability. The algorithms were evaluated using the Database of Faces which comprises 40 subjects and with a total size of 400 images. Experimental results show that ANN reasonably improves the performance of PCA and LDA method. LDA-NN achieves an average recognition accuracy of 95.8%.
|Item Type:||Book Section|
|Additional Information:||2009 4th IEEE Conference on Industrial Electronics and Applications, ICIEA 2009; Xi'an; 25 May 2009 through 27 May 2009|
|Uncontrolled Keywords:||linear discriminant analysis, neural networks error backpropagation, principal component analysis|
|Subjects:||T Technology > TK Electrical engineering. Electronics Nuclear engineering|
|Deposited By:||Liza Porijo|
|Deposited On:||14 Jul 2011 01:15|
|Last Modified:||14 Jul 2011 01:15|
Repository Staff Only: item control page