scholarly journals PCA and LDA Based Neural Networks for Human Face Recognition

10.5772/4833 ◽  
2007 ◽  
Author(s):  
Alaa Eleyan ◽  
Hasan Demirel
Keyword(s):  
Neural Networks ◽  
Face Recognition ◽  
Human Face ◽  
Human Face Recognition

MULTI-MODULAR NEURAL NETWORK ARCHITECTURES: APPLICATIONS IN OPTICAL CHARACTER AND HUMAN FACE RECOGNITION

1993 ◽  
Vol 07 (04) ◽  
pp. 721-755 ◽  
Author(s):  
FRANÇOISE FOGELMAN SOULIE ◽  
EMMANUEL VIENNET ◽  
BERTRAND LAMY
Keyword(s):  
Neural Networks ◽  
Face Recognition ◽  
Optical Character Recognition ◽  
General Mechanism ◽  
Human Face ◽  
Human Face Recognition ◽  
Trade Off ◽  
Practical Applications ◽  
Optical Character ◽  
Modular Architectures

In practical applications, recognition accuracy is sometimes not the only criterion; capability to reject erroneous patterns might also be needed. We show that there is a trade-off between these two properties. An efficient solution to this trade-off is brought about by the use of different algorithms implemented in various modules, i.e. multi-modular architectures. We present a general mechanism for designing and training multi-modular architectures, integrating various neural networks into a unique pattern recognition system, which is globally trained. It is possible to realize, within the system, feature extraction and recognition in successive modules which are cooperatively trained. We discuss various rejection criteria for neural networks and multi-modular architectures. We then give two examples of such systems, study their rejection capabilities and show how to use them for segmentation. In handwritten optical character recognition, our system achieves performances at state-of-the-art level, but is eight times faster. In human face recognition, our system is intended to work in the real world.

Face Recognition Based on Wavelet Neural Network

2011 ◽  
Vol 204-210 ◽  
pp. 216-219
Author(s):  
Hong Zhang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Face Recognition ◽  
Wavelet Transform ◽  
Recognition Rate ◽  
Human Face ◽  
Recognition Method ◽  
Human Face Recognition ◽  
The Face ◽  
Simulation Testing

It's well known that the technology of human face recognition has become a hot topicin pattern recognition field. Though a lot of progress has been made by many researchersthese years, many key problems still have to be solved in order to popularize the application of face recognition because of the complexity of face recognition. The background, development and main methods of face recognition are introducedfirstly in this paper, then a face recognition method which is based on wavelet transform,KL transform and BP neural networks is used in the paper.Here the face feature extraction includes wavelet transform and KL transform.Moreover,the recognition classifier is BP neural networks.The simulation testing in the paper holds good recognition rate.

Human face recognition based on multi-features using neural networks committee

2004 ◽  
Vol 25 (12) ◽  
pp. 1351-1358 ◽  
Author(s):  
Zhong-Qiu Zhao ◽  
De-Shuang Huang ◽  
Bing-Yu Sun
Keyword(s):  
Neural Networks ◽  
Face Recognition ◽  
Human Face ◽  
Human Face Recognition
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