Face detection from greyscale images using details from categorized wavelet coefficients as features for a dynamic counterpropagation network

Lee Seng Yeong, Li Minn Ang, Kah Phooi Seng

Research output: Book chapter/Published conference paperConference paper

2 Citations (Scopus)

Abstract

A dynamic counterpropagation network based on the forward only counterpropagation network (CPN) is applied to face detection in this paper. The network, called the dynamic supervised forward-propagation network (DSFPN) trains using a supervised algorithm and can grow dynamically during training allowing subclasses in the training data to be learnt. The network is trained using the categorized wavelet coefficients of the image as features of the image. The results suggests a 98% correct detection rate can be achieved with 4% false positives by increasing network complexity.

Original languageEnglish
Title of host publicationProceedings - 2007 International Conference on Computational Intelligence and Security, CIS 2007
Pages315-319
Number of pages5
DOIs
Publication statusPublished - 01 Dec 2007
Event2007 International Conference on Computational Intelligence and Security, CIS'07 - Harbin, Heilongjiang, China
Duration: 15 Dec 200719 Dec 2007

Conference

Conference2007 International Conference on Computational Intelligence and Security, CIS'07
CountryChina
CityHarbin, Heilongjiang
Period15/12/0719/12/07

Fingerprint Dive into the research topics of 'Face detection from greyscale images using details from categorized wavelet coefficients as features for a dynamic counterpropagation network'. Together they form a unique fingerprint.

Cite this