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

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.

[1]  Oscar Déniz-Suárez,et al.  A comparison of face and facial feature detectors based on the Viola–Jones general object detection framework , 2011, Machine Vision and Applications.

[2]  Andreas Ernst,et al.  Face detection with the modified census transform , 2004, Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004. Proceedings..

[3]  K. S. Venkatesh,et al.  Automatic and Robust Detection of Facial Features in Frontal Face Images , 2011, 2011 UkSim 13th International Conference on Computer Modelling and Simulation.

[4]  Anil K. Jain,et al.  Face Detection in Color Images , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Lloyd N. Trefethen,et al.  Barycentric Lagrange Interpolation , 2004, SIAM Rev..

[6]  Claudio A. Perez,et al.  Real-Time Template Based Face and Iris Detection on Rotated Faces , 2009 .

[7]  Paul A. Viola,et al.  Robust Real-Time Face Detection , 2001, International Journal of Computer Vision.

[8]  Inho Choi,et al.  Eye Detection and Eye Blink Detection Using AdaBoost Learning and Grouping , 2011, 2011 Proceedings of 20th International Conference on Computer Communications and Networks (ICCCN).

[9]  Andry Rakotonirainy,et al.  Affordable visual driver monitoring system for fatigue and monotony , 2004, 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583).

[10]  O. Jacobs,et al.  Introduction to Control Theory , 1976, IEEE Transactions on Systems, Man, and Cybernetics.

[11]  Youding Zhu,et al.  Head pose estimation for driver monitoring , 2004, IEEE Intelligent Vehicles Symposium, 2004.

[12]  Yongsheng Gao,et al.  Face recognition across pose: A review , 2009, Pattern Recognit..

[13]  Zhiwei Zhu,et al.  Robust real-time eye detection and tracking under variable lighting conditions and various face orientations , 2005, Comput. Vis. Image Underst..

[14]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[15]  Klaus J. Kirchberg,et al.  Robust Face Detection Using the Hausdorff Distance , 2001, AVBPA.

[16]  T. Ohmi,et al.  An Accurate Eye Detection Method Using Elliptical Separability Filter and Combined Features , 2009 .

[17]  Bin Hu,et al.  Real-Time Eye Locating and Tracking for Driver Fatigue Detection , 2010 .

[18]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.

[19]  James M. Rehg,et al.  Fast Asymmetric Learning for Cascade Face Detection , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[20]  Reinhard Klette,et al.  3D Cascade of Classifiers for Open and Closed Eye Detection in Driver Distraction Monitoring , 2011, CAIP.

[21]  Hong Liu,et al.  Robust real-time eye detection and tracking for rotated facial images under complex conditions , 2010, 2010 Sixth International Conference on Natural Computation.