Abstract

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

Eye center localization and gaze gesture recognition for human–computer interaction

Wenhao Zhang, Melvyn L. Smith, Lyndon N. Smith, and Abdul Farooq
J. Opt. Soc. Am. A 33(3) 314-325 (2016)

High-speed and robust infrared-guiding multiuser eye localization system for autostereoscopic display

Xicai Li, Qinqin Wu, Bangpeng Xiao, Xuanyi Liu, Chen Xu, Xueling Li, Bin Xu, and Yuanqing Wang
Appl. Opt. 59(14) 4199-4208 (2020)

Double random phase encoding for cancelable face and iris recognition

Randa F. Soliman, Ghada M. El Banby, Abeer D. Algarni, Mohamed Elsheikh, Naglaa F. Soliman, Mohamed Amin, and Fathi E. Abd El-Samie
Appl. Opt. 57(35) 10305-10316 (2018)

Iris segmentation using an edge detector based on fuzzy sets theory and cellular learning automata

Afshin Ghanizadeh, Amir Atapour Abarghouei, Saman Sinaie, Puteh Saad, and Siti Mariyam Shamsuddin
Appl. Opt. 50(19) 3191-3200 (2011)

New automated iris image acquisition method

Kang Ryoung Park
Appl. Opt. 44(5) 713-734 (2005)