Adaptive Haar-like classifier for eye status detection under non-ideal lighting conditions

Authors:
Mahdi Rezaei;Reinhard Klette
Affiliations:
The University of Auckland, Auckland, New Zealand;The University of Auckland, Auckland, New Zealand
Venue:
Proceedings of the 27th Conference on Image and Vision Computing New Zealand
Year:
2012

Citing 11
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
Robust Real-Time Face Detection

International Journal of Computer Vision
Fast Asymmetric Learning for Cascade Face Detection

IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Face detection with the modified census transform

FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture 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
A comparison of face and facial feature detectors based on the Viola–Jones general object detection framework

Machine Vision and Applications
3D cascade of classifiers for open and closed eye detection in driver distraction monitoring

CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part II

Quantified Score

Hi-index	0.00

Visualization

Abstract

The paper introduces a novel methodology to enhance the accuracy, performance and effectiveness of Haar-like classifiers, especially for complicated lighting conditions. Performing a statistical intensity analysis on input image sequences, the technique provides a very fast and robust eye-status detection via a low-resolution VGA camera, without application of any infrared illumination or image enhancement. We report about a test for driver monitoring under real-world conditions also featuring challenging lighting conditions such as 'very bright' at daytime or 'very dark' or 'artificial lighting' at night. An adaptive Haar classifier adjusts the detection parameters according to dynamic level-based intensity measurements in given regions of interest. Experimental results and performance evaluation on various datasets show a higher detection rate compared to standard Viola-Jones classifiers.