Face detection from greyscale images using details from categorized wavelet coefficients as features for a dynamic supervised forward propagation network
A dynamic counterpropagation network based on the forward only counterpropagation network (CPN) is applied as the classifier for face detection. The network, called the dynamic supervised forward-propagation network (DSFPN) trains using a supervised algorithm that grows dynamically during training a...
| Main Authors: | , , , |
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| Format: | Journal Article |
| Published: |
2009
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| Online Access: | http://hdl.handle.net/20.500.11937/42759 |
| Summary: | A dynamic counterpropagation network based on the forward only counterpropagation network (CPN) is applied as the classifier for face detection. The network, called the dynamic supervised forward-propagation network (DSFPN) trains using a supervised algorithm that grows dynamically during training allowing subclasses in the training data to be learnt. The network is trained using a reduced dimensionality categorized wavelet coefficients of the image data. Experimental results obtained show that a 94% correct detection rate can be achieved with less than 6% false positives. © 2009 World Scientific Publishing Company. |
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