Iris recognition using combined support vector machine and Hamming distance approach

• A combination of SVM & Hamming distance approach is used for iris recognition.
• The proposed method has a recognition rate of 99.91% & 99.88% on CASIA & Chek image database.
• The accuracy is excellent for the CASIA & Chek image database in terms of FAR & FRR.

Iris based authentication system is essentially a pattern recognition technique that makes use of iris patterns, which are statistically unique, for the purpose of personal identification. In this study, a novel method for recognition of iris patterns is considered by using a combination of support vector machine and Hamming distance. The zigzag collarette area of the iris is selected for iris feature extraction because it captures the most important areas of iris complex pattern and higher recognition rate is achieved. The proposed approach also used parabola detection and trimmed median filter for the purpose of eyelid and eyelash detection & removal, respectively. The proposed method is computationally effective as well as reliable with a recognition rate of 99.91% and 99.88% on CASIA and Chek image database respectively.