In this paper, a robust method is developed to locate the irises of both eyes. The method doesn't put any restrictions on the background. The method is based on the AdaBoost algorithm for face and eye candidate points detection. Candidate points are tuned such that two candidate points are exactly in the centers of the irises. Mean crossing function and convolution template are proposed to filter out candidate points and select the iris pair. The advantage of using this kind of hybrid method is that AdaBoost is robust to different illumination conditions and backgrounds. The tuning step improves the precision of iris localization while the convolution filter and mean crossing function reliably filter out candidate points and select the iris pair. The proposed structure is evaluated on three public databases, Bern, Yale and BioID. Extensive experimental results verified the robustness and accuracy of the proposed method. Using the Bern database, the performance of the proposed algorithm is also compared with some of the existing methods.

© 2009 Optical Society of Korea

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2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'01)

• P. Viola, and M. Jones, "Rapid object detection using a boosted cascade of simple features," in Proc. Computer Vision and Pattern Recognition Conference 2001 (Hawaii, USA, 2001), vol. 1, pp. 511-518 [CrossRef]

Acoustics, Speech, and Signal Processing, 2000. ICASSP '00. Proceedings. 2000 IEEE International Conference on

• M. Pardas, "Extraction and tracking of the eyelids," in Proc. IEEE Int. Conf on Acoustics, Speech, and Signal Processing (Istanbul, Turkey, 2000), pp. 2357-2360 [CrossRef]

Automatic Face and Gesture Recognition, 2000. Proceedings. Fourth IEEE International Conference on

• Y. Tian, T. Kanade, and J. F. Cohn, "Dual-state parametric eye tracking," in Proc. Int. Conf. on Face and Gesture Recognition (Grenoble, France, 2000), pp. 110-115 [CrossRef]

IEEE Trans. Image Process

• C.-H. Lin and J.-Ling, "Automatic facial feature extraction by genetic algorithms," IEEE Trans. Image Process 8, 1057–7149 (1999)

in Proc. IEEE Intell. Vehicles Symposium

• M. Betke and W. J. Mullally, "Preliminary investigation of real-time monitoring of a driver in city traffic," in Proc. IEEE Intell. Vehicles Symposium (Dearborn, MI, USA, 2000), pp. 563-568

International Journal of Computer Vision

• A. L. Yuille, P. W. Hallinan, and D. S. Cohen, "Feature extraction from faces using deformable templates," Int. Journal of Computer Vision 8, 99-111 (1992) [CrossRef]

Knowledge-Based Intelligent Engineering Systems and Allied Technologies, 2000. Proceedings. Fourth International Conference on

• L. Zhang and P. Lenders, "Knowledge-based eye detection for human face recognition," in Proc. the 4th Int. Conf. on Knowledge-Bases Intelligent Systems & Allied Technologies (Brighton, UK, 2000), vol. 1, pp. 117-120 [CrossRef]

Lecture Notes in Computer Science

• M. Castrilljon-Santana, J. Lorenzo-Navarro, O. Djeniz-Sujarez, J. Isern-Gonzjalez, and A. Falcjon-Martel, "Multiple face detection at different resolutions for perceptual user interfaces," in Proc. 2nd Iberian Conference on Pattern Recognition and Image Analysis 2005, J.S. Marques et al. Ed., LNCS, (Estoril, Portugal, 2005), vol. 3522, pp. 445-452 [CrossRef]
• O. Jesorsky, K. J. Kirchberg, and R. W. Frischholz, "Robust face detection using the hausdorff distance," in Proc. the Third International Conference on Audio- and Video-based Biometric Person Authentication 2001, (Halmstad, Sweden, 2001), pp. 90-95 [CrossRef]

Pattern Analysis and Machine Intelligence, IEEE Transactions on

• Y. Tian, T. Kanade, and J. F. Cohn, "Recognizing action units for facial expression analysis," IEEE Trans. Pattern Anal. Mach. Intell. 23, 97–115 (2001) [CrossRef]
• R. L. Hsu, M. Abdel-Mottaleb, and A. K. Jain, "Face detection in color images," IEEE Trans. Pattern Anal. Mach. Intell. 24, 696–706 (2002) [CrossRef]

Pattern Recognition

• C. C. Han, H. Y. M. Liao, G. J. Yu, and L. H. Chen, "Fast face detection via morphology-based pre-processing," Pattern Recognition 33, 1701–1712 (2000) [CrossRef]
• J. Wu and Z. H. Zhou, "Efficient face candidates selector for face detection," Pattern Recognition 36, 1175–1186 (2003) [CrossRef]
• B. Fasel and J. Luettin, "Automatic facial expression analysis: a survey," Pattern Recognition 36, 259–275 (2003) [CrossRef]
• D. Maio and D. Maltoni, "Real-time face location on grayscale static images," Pattern Recognition 33, 1525-1539 (2000) [CrossRef]
• T. Kawaguchi and M. Rizon, "Iris detection using intensity and edge information," Pattern Recognition 36, 549–562 (2003) [CrossRef]
• J. Song, Z. Chi, and J. Liu, "A robust eye detection method using combined binary edge and intensity information," Pattern Recognition 39, 1110-1125 (2006) [CrossRef]
• Z. H. Zhou and X. Geng, "Projection functions for eye detection," Pattern Recognition 37, 1049–1056 (2004) [CrossRef]

Pattern Recognition Letters

• G. C. Feng and P. C. Yuen, "Variance projection function and its application to eye detection for human face recognition," Pattern Recognition Lett. 19, 899-906 (1998) [CrossRef]

Systems and Computers in Japan

• K. Fukui and O. Yamaguchi, "Facial feature point extraction method based on combination of shape extraction and pattern matching," Systems and Computers in Japan 29, 49–58 (1998) [CrossRef]

Other

• http://iamwww.unibe.ch/<TEX>$\sim$</TEX>kiwww/staff/achermann.html
• http://www.bioid.com/downloads/facedb/index.php
• http://cvc.yale.edu/projects/yalefaces/yalefaces.html

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