Authors:
Mario Konrad
1
;
Herbert Stögner
1
;
Andreas Uhl
2
and
Peter Wild
2
Affiliations:
1
Carinthia Tech Institute, Austria
;
2
University of Salzburg, Austria
Keyword(s):
Iris recognition, Multibiometrics, Serial classifier combination, Rotation compensation.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computer Vision, Visualization and Computer Graphics
;
Data Manipulation
;
Feature Extraction
;
Features Extraction
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Image and Video Analysis
;
Image Formation and Preprocessing
;
Implementation of Image and Video Processing Systems
;
Informatics in Control, Automation and Robotics
;
Methodologies and Methods
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing, Sensors, Systems Modeling and Control
;
Soft Computing
Abstract:
Rotation compensation is one of the computational bottlenecks in large scale iris-based identification schemes, since a significant amount of Hamming distance computations is required in a single match due to the necessary shifting of the iris codes to compensate for eye tilt. To cope with this problem, a serial classifier combination approach is proposed for iris-based identification, combining rotation-invariant pre-selection with a traditional rotation compensating iris code-based scheme. The primary aim, a reduction of computational complexity, can easily be met - at comparable recognition accuracy, the computational effort required is reduced to 20% or even less of the fully fledged iris code based scheme. As a by-product, the recognition accuracy is shown to be additionally improved in open-set scenarios.