An iris detection method based on structure information

Authors:
Jiali Cui;Tieniu Tan;Xinwen Hou;Yunhong Wang;Zhuoshi Wei
Affiliations:
Center for Biometrics and Security Research, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, P.R. China;Center for Biometrics and Security Research, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, P.R. China;Center for Biometrics and Security Research, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, P.R. China;Center for Biometrics and Security Research, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, P.R. China;Center for Biometrics and Security Research, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, P.R. China
Venue:
IWBRS'05 Proceedings of the 2005 international conference on Advances in Biometric Person Authentication
Year:
2005

Citing 6
Cited 0

Biometrics, Personal Identification in Networked Society: Personal Identification in Networked Society

Biometrics, Personal Identification in Networked Society: Personal Identification in Networked Society
Rendering: Parallelization of Bresenham's Line and Circle Algorithms

IEEE Computer Graphics and Applications
High Confidence Visual Recognition of Persons by a Test of Statistical Independence

IEEE Transactions on Pattern Analysis and Machine Intelligence
Personal Identification Based on Iris Texture Analysis

IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Real-Time Face Detection

International Journal of Computer Vision
A novel method to extract features for iris recognition system

AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we propose an iris detection method to determine iris existence. The method extracts 4 types of features, i.e., contrast feature, symmetric feature, isotropy feature and disconnectedness feature. Adaboost is adopted to combine these features to build a strong cascaded classifier. Experiments show that the performance of the method is promising in terms of high speed, accuracy and device independence.