Improved segmentation algorithm and further optimization for iris recognition
- Resource Type
- Conference
- Authors
- Uka, Arban; Roci, Albana; Koc, Oktay
- Source
- IEEE EUROCON 2017 -17th International Conference on Smart Technologies Smart Technologies, IEEE EUROCON 2017 -17th International Conference on. :85-88 Jul, 2017
- Subject
- Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineered Materials, Dielectrics and Plasmas
Fields, Waves and Electromagnetics
Nuclear Engineering
Photonics and Electrooptics
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Iris recognition
Image segmentation
Databases
Encoding
Image coding
Algorithm design and analysis
Data mining
Segmentation
Equal Error Rate (EER)
Accuracy
- Language
Iris recognition is a biometric authentication system proving vital for ensuring security and has been employed as an important case to test the algorithms developed in pattern recognition. The unique circular shape of the iris and its time invariance makes it a versatile technique that has an accuracy that can be mathematically proven. Here in this work we propose a new segmentation technique and two new encoding schemes. The newly proposed techniques are tested on the best, the worst and on all the irises of two widely known databases (CASIA and IIT Delhi database) and the results are compared with the classical segmentation and classical encoding schemes. The segmentation is improved and as a result the accuracy and equal error rate also. In CASIA database the use of the new segmentation improves the EER from 3.14% to 0.82% (on all 756 images of the dataset). When tested on the whole IIT iris database, the EER is improved from 3.88% to 0.34%; and on the worst images of IIT EER is improved from 13.30% to 1.00%.