An eye detection system based on neural autoassociators

Authors:
Monica Bianchini;Lorenzo Sarti
Affiliations:
Dipartimento di Ingegneria dell'Informazione, Università degli Studi di Siena, Siena, Italy;Dipartimento di Ingegneria dell'Informazione, Università degli Studi di Siena, Siena, Italy
Venue:
ANNPR'06 Proceedings of the Second international conference on Artificial Neural Networks in Pattern Recognition
Year:
2006

Citing 7
Cited 3

Face Detection in Color Images

IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital Image Processing

Digital Image Processing
Real-time eye, gaze, and face pose tracking for monitoring driver vigilance

Real-Time Imaging
Skin-Color Modeling and Adaptation

ACCV '98 Proceedings of the Third Asian Conference on Computer Vision-Volume II
A New Method for Eye Extraction from Facial Image

DELTA '04 Proceedings of the Second IEEE International Workshop on Electronic Design, Test and Applications
Recursive neural networks learn to localize faces

Pattern Recognition Letters - Special issue: Artificial neural networks in pattern recognition
Learning in multilayered networks used as autoassociators

IEEE Transactions on Neural Networks

Eye localization for face matching: is it always useful and under what conditions?

CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
Eye localization in low and standard definition content with application to face matching

Computer Vision and Image Understanding
The facial texture analysis for the automatic portrait drawing

Pattern Recognition

Quantified Score

Hi-index	0.00

Visualization

Abstract

Automatic eye tracking is a challenging task, with numerous applications in biometrics, security, intelligent human–computer interfaces, and driver's sleepiness detection systems. Eye localization and extraction is, therefore, the first step to the solution of such problems. In this paper, we present a new method, based on neural autoassociators, to solve the problem of detecting eyes from a facial image. A subset of the AR Database, collecting individuals both with or without glasses and with open or closed eyes, has been used for experiments and benchmarking. Preliminary experimental results are very promising and demonstrate the efficiency of the proposed eye localization system.