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...

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Main Authors: Yeong, L., Ang, L., Lim, Hann, Seng, K.
Format: Journal Article
Published: 2009
Online Access:http://hdl.handle.net/20.500.11937/42759
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author Yeong, L.
Ang, L.
Lim, Hann
Seng, K.
author_facet Yeong, L.
Ang, L.
Lim, Hann
Seng, K.
author_sort Yeong, L.
building Curtin Institutional Repository
collection Online Access
description 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.
first_indexed 2025-11-14T09:13:18Z
format Journal Article
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institution Curtin University Malaysia
institution_category Local University
last_indexed 2025-11-14T09:13:18Z
publishDate 2009
recordtype eprints
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spelling curtin-20.500.11937-427592017-09-13T14:29:24Z Face detection from greyscale images using details from categorized wavelet coefficients as features for a dynamic supervised forward propagation network Yeong, L. Ang, L. Lim, Hann Seng, K. 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. 2009 Journal Article http://hdl.handle.net/20.500.11937/42759 10.1142/S0218001409006977 restricted
spellingShingle Yeong, L.
Ang, L.
Lim, Hann
Seng, K.
Face detection from greyscale images using details from categorized wavelet coefficients as features for a dynamic supervised forward propagation network
title Face detection from greyscale images using details from categorized wavelet coefficients as features for a dynamic supervised forward propagation network
title_full Face detection from greyscale images using details from categorized wavelet coefficients as features for a dynamic supervised forward propagation network
title_fullStr Face detection from greyscale images using details from categorized wavelet coefficients as features for a dynamic supervised forward propagation network
title_full_unstemmed Face detection from greyscale images using details from categorized wavelet coefficients as features for a dynamic supervised forward propagation network
title_short Face detection from greyscale images using details from categorized wavelet coefficients as features for a dynamic supervised forward propagation network
title_sort face detection from greyscale images using details from categorized wavelet coefficients as features for a dynamic supervised forward propagation network
url http://hdl.handle.net/20.500.11937/42759