Fast eye localization based on pixel differences

A novel fast eye localization algorithm based on pixel differences is presented, which is suitable for face recognition system on mobile device. It is based on the fact that eyeball is dark and round. A binary eye map is obtained by choosing those pixels darker than surrounding; then it is filtered by a rank order filter; connected regions in the eye map are then labeled by their geometric centers; best suitable eyeball pair is selected based on a set of geometric constraints. If no eyeball pair is detected, the algorithm is repeated iteratively until one pair is found. The algorithm is fast since it converts the gray level image to a binary eye map at the beginning. The algorithm is tested on our own face database, which consists of 4095 images of size 250×200. Detection rate is 93.04% when the tolerance is 0.7 times of eyeball width.

[1]  Shin'ichi Satoh,et al.  A hybrid classifier for precise and robust eye detection , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[2]  Yunhui Liu,et al.  An Efficient Face Normalization Algorithm Based on Eyes Detection , 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[3]  Rainer Stiefelhagen,et al.  Gaze tracking for multimodal human-computer interaction , 1997, 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[4]  Yen-Wei Chen,et al.  A Robust Eye Detection and Tracking Technique Using Gabor Filters , 2007, Third International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP 2007).

[5]  Young Shik Moon,et al.  An Effective Method for Eye Detection Based on Texture Information , 2007, 2007 International Conference on Convergence Information Technology (ICCIT 2007).

[6]  Daw-Tung Lin,et al.  Real-time eye detection using face-circle fitting and dark-pixel filtering , 2004, 2004 IEEE International Conference on Multimedia and Expo (ICME) (IEEE Cat. No.04TH8763).

[7]  Hyeonjoon Moon,et al.  The FERET evaluation methodology for face-recognition algorithms , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[8]  T. D'Orazio,et al.  A neural system for eye detection in a driver vigilance application , 2004, Proceedings. The 7th International IEEE Conference on Intelligent Transportation Systems (IEEE Cat. No.04TH8749).

[9]  Hyeonjoon Moon,et al.  The FERET Evaluation Methodology for Face-Recognition Algorithms , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  Shyan-Ming Yuan,et al.  Next Generation Notification System Integrating Instant Messengers and Web Service , 2007, 2007 International Conference on Convergence Information Technology (ICCIT 2007).