Iris Segmentation and Recognition using Dense Fully Convolutional Network and Multiclass Support Vector Machine Classifier

Main Article Content

D.Meenakshi, et.al

Abstract

Iris Recognition has been one of the most robust means of biometric recognition. In recent years, there are so many researches in iris recognition using deep learning. In this paper, deep neural networks are used for segmentation, feature extraction and classification. The Dense Fully Convolutional Network is used for segmentation. The segmented iris is normalized which is then converted to Gabor wavelet for feature extraction. The Gabor features are extracted using multiclass Support Vector Machine classifier. The proposed method is tested on CASIA Iris 1000 and IITD datasets. The performance of the proposed method is evaluated using accuracy, precision and recall measures. It is also compared with state-of-the-art methods.

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...

Article Details

How to Cite
et.al, D. . (2021). Iris Segmentation and Recognition using Dense Fully Convolutional Network and Multiclass Support Vector Machine Classifier. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 12(13), 5418–5428. https://doi.org/10.17762/turcomat.v12i13.9786
Section
Research Articles