A fast neural-based eye detection system

Fok Hing Chi Tivive*, Abdesselam Bouzerdoum

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

9 Citations (Scopus)

Abstract

This paper presents a fast eye detection system which is based on an artificial neural network known as the shunting inhibitory convolutional neural network, or SICoNNet for short. With its two-dimensional network architecture and the use of convolution operators, the eye detection system processes an entire input image and generates the location map of the detected eyes at the output. The network consists of 479 trainable parameters which are adapted by a modified Levenberg-Marquardt training algorithm in conjunction with a bootstrap procedure. Tested on 180 real images, with 186 faces, the accuracy of the eye detector reaches 96.8% with only 38 false detections.

Original languageEnglish
Title of host publicationProceedings of 2005 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2005
Pages641-644
Number of pages4
Publication statusPublished - 2005
Externally publishedYes
Event2005 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2005 - Hong Kong, China
Duration: 13 Dec 200516 Dec 2005

Publication series

NameProceedings of 2005 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2005
Volume2005

Conference

Conference2005 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2005
Country/TerritoryChina
CityHong Kong
Period13/12/0516/12/05

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