Zhiming Qian
ABSTRACT
The eye localization is a fundamental step in human-computer interaction and automatic face recognition. In this paper, a new eye detection method for face images is proposed. Firstly, the rough eye positions are found by Gabor transformation and cluster analysis. Secondly, a detection of pupil centers will be continued by applying two neighborhood operators in the rough eye regions. A subset of the color FERET database and the Faces 1999 database are used to evaluate the proposed method. Results of experiments show that our method is robust and efficient.
- P. Viola and M. J. Jones, Robust real-time face detection. Internationnal Journal of Computer Vision, 57(2): 137--154, 2004. Google Scholar
Digital Library
- C. Liu, and H. Wechsler, Independent Component Analysis of Gabor Features for Face Recognition. IEEE Transactions on Neural Networks, 14(4): 919--928, 2003. Google ScholarDigital Library
- A. Timo, H. Abdenour, and P. Matti, Face description with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(12): 2037--2041, 2006. Google ScholarDigital Library
- P. J. Phillips, H. Moon, S. A. Rizvi, P. J. Rauss, The FERET evaluation methodology for face recognition algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(10): 1090--1104, 2000. Google ScholarDigital Library
- Q. Ji, H. Wechsler, A. Duchowski, and M. Flickner, Eye detection and tracking: Special issue. Computer Vision and Image Understanding, 98(1): 1--3, 2005. Google ScholarDigital Library
- A. Haro, M. Flickner, and I. Essa, Detecting and tracking eyes by using their physiological properties, dynamics, and appearance. In: IEEE International Conference on Computer Vision and Pattern Recognition, 1: 163--168, 2000.Google ScholarCross Ref
- Z. Zhu, Q. Ji, and K. Fujimura, Combining Kalman filtering and mean shift for real time eye tracking under active IR illumination, In: IEEE International Conference on Pattern Recognition, 4: 318--321, 2002.Google Scholar
- Z. Zhou, and X. Geng, Projection functions for eye detection. Pattern Recognition, 37(5): 1049--1056, 2004.Google ScholarCross Ref
- K. M. Lam, and H. Yan, Locating and extracting the eye in human face images. Pattern Recognition, 29(5): 771--779, 1996. Google ScholarDigital Library
- T. d'Orazio, M. Leo, G. Cicirelli, and A. Distante, An algorithm for real time eye detection in face images. In: IEEE International Conference on Pattern Recognition, 3:278--281, 2004. Google ScholarDigital Library
- P. Wang, M. Green, Q. Ji, and J. Waymanm, Automatic eye detection and its validation. In: IEEE Workshop on Face Recognition Grand Challenge Experiments, In association with CVPR 2005, San Diego, CA, 2005. Google ScholarDigital Library
- X. Tang, Z. Ou, T. Su, H. Sun, and P. Zhao, Robust precise eye location by Adaboost and SVM techniques. In: J. Wang, X. Liao, and Z. Yi (Eds). Lecture Notes in Computer Science, Berlin: Springer, 3497: 93--98, 2005. Google ScholarDigital Library
- H. Schneiderman, and T. Kanade, A statistical method for 3D object detection applied to faces and cars. In: IEEE International Conference on Computer Vision and Pattern Recognition, 1: 746--751, 2000.Google ScholarCross Ref
- H. A. Rowley, S. Baluja, T. Kanade, Neural network-based face detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(1): 23--38, 1998. Google ScholarDigital Library
- Y. Freund, R. E. Schapire, A decision-theoretic generalization of online learning and an application to boosting. Journal of Computer and System Sciences, 55(1): 119--139, 1997. Google ScholarDigital Library
- T. S. Lee, Image representation using 2D Gabor wavelets, IEEE Transactions on Pattern Analysis and Machine Intelligence, 18(10): 959--971, 1996. Google ScholarDigital Library
- P. J. Phillips, H. Wechsler, J. Huang, and P. Rauss, The FERET database and evaluation procedure for face recognition algorithms. Image and Vision Computing, 16(5): 295--306, 1998.Google ScholarCross Ref
- The Faces 1999 database {http://www.vision.caltech.edu/html-files/archive.html}.Google Scholar
- O. Jesorsky, K. Kirchberg, and R. Frischholz, Robust face detection using the Hausdorff distance. In: J. Bigun, F. Smeraldi (Eds). Lecture Notes in Computer Science, Berlin: Springer, 2091: 90--95, 2001. Google ScholarDigital Library
Index Terms
- Precise eye detection on frontal view face image
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