Novel adaptive eye detection and tracking for challenging lighting conditions

Authors:
Mahdi Rezaei;Reinhard Klette
Affiliations:
The .enpeda.. Project, The University of Auckland, New Zealand;The .enpeda.. Project, The University of Auckland, New Zealand
Venue:
ACCV'12 Proceedings of the 11th international conference on Computer Vision - Volume 2
Year:
2012

Citing 10
Cited 0

A decision-theoretic generalization of on-line learning and an application to boosting

Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Face Detection in Color Images

IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Face Detection Using the Hausdorff Distance

AVBPA '01 Proceedings of the Third International Conference on Audio- and Video-Based Biometric Person Authentication
Fast Asymmetric Learning for Cascade Face Detection

IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Detection in Color Images Using AdaBoost Algorithm Based on Skin Color Information

WKDD '08 Proceedings of the First International Workshop on Knowledge Discovery and Data Mining
Face recognition across pose: A review

Pattern Recognition
Robust real-time eye detection and tracking under variable lighting conditions and various face orientations

Computer Vision and Image Understanding - Special issue on eye detection and tracking
A data mining approach to face detection

Pattern Recognition
Automatic and Robust Detection of Facial Features in Frontal Face Images

UKSIM '11 Proceedings of the 2011 UKSim 13th International Conference on Modelling and Simulation
Handbook of Face Recognition

Handbook of Face Recognition

Quantified Score

Hi-index	0.00

Visualization

Abstract

The paper develops a novel technique that significantly improves the performance of Haar-like feature-based object detectors in terms of speed, detection rate under difficult lighting conditions, and reduced number of false-positives. The method is implemented and validated for driver monitoring under very dark, very bright, and normal conditions. The framework includes a fast adaptive detector designed to cope with rapid lighting variations, as well as an implementation of a Kalman filter for reducing the search region and indirect support of eye monitoring and tracking. The proposed methodology effectively works under low-light conditions without using infrared illumination or any other extra lighting support. Experimental results, performance evaluation, and comparing a standard Haar-like detector with the proposed adaptive eye detector, show noticeable improvements.