Fast Iris localization using Haar-like features and AdaBoost algorithm

Traditional iris recognition methods, which are still preferred against artificial intelligence (AI) approaches in practical applications, are often required to capture high-grade iris samples by an iris scanner for accurate subsequent processing. To reduce the system cost for mass deployment of iri...

Full description

Saved in:
Bibliographic Details
Published in:Multimedia tools and applications 2020-12, Vol.79 (45-46), p.34339-34362
Main Authors: Lin, Yi-Nan, Hsieh, Tsang-Yen, Huang, Jr-Jen, Yang, Cheng-Ying, Shen, Victor R. L., Bui, Hai Hoang
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Biometric recognition systems
Cameras
Classifiers
Computer Communication Networks
Computer Science
Data Structures and Information Theory
Localization
Localization method
Machine learning
Multimedia Information Systems
Post-production processing
Special Purpose and Application-Based Systems
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Traditional iris recognition methods, which are still preferred against artificial intelligence (AI) approaches in practical applications, are often required to capture high-grade iris samples by an iris scanner for accurate subsequent processing. To reduce the system cost for mass deployment of iris recognition, pricey scan devices can be replaced by the average quality cameras combined with additional processing algorithm. In this paper, we propose a Haar-like-feature-based iris localization method to quickly detect the location of human iris in the images captured by low-cost cameras for the ease of post-processing stages. The AdaBoost algorithm was chosen as a learning method for training a cascade classifier using Haar-like features, which was then utilized to detect the iris position. The experimental results have shown acceptable accuracy and processing speed for this novel cascade classifier. This achievement stimulates us to implement this novel capturing device in our iris recognition.
ISSN:1380-7501
1573-7721
DOI:10.1007/s11042-020-08907-5