Semantic Segmentation of Iris using U-Net in Deep Learning
Dugimpudi Abhishek Reddy1, Deepak Yadav2, Nishi Yadav3, Devendra Kumar Singh4
1Dugimpudi Abhishek Reddy, Department of CSE, School of Studies in Engineering and Technology, Guru Ghasidas Vishwavidyalaya, Bilaspur, India.
2Deepak Yadav, Department of CSE, School of Studies in Engineering and Technology, Guru Ghasidas Vishwavidyalaya, Bilaspur, India.
3Nishi Yadav*, Department of CSE, School of Studies in Engineering and Technology, Guru Ghasidas Vishwavidyalaya, Bilaspur, India.
4Devendra Kumar Singh, Department of CSE, School of Studies in Engineering and Technology, Guru Ghasidas Vishwavidyalaya, Bilaspur, India.
Manuscript received on May 20, 2020. | Revised Manuscript received on May 22, 2020. | Manuscript published on May 30, 2020. | PP: 2024-2028 | Volume-9 Issue-1, May 2020. | Retrieval Number: A2614059120/2020©BEIESP | DOI: 10.35940/ijrte.A2614.059120
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In the field of medicine, iris segmentation has become a great field of interest from the past few years. Iris segmentation is also largely used in iris recognition systems [3] which are extensively used in security control [1][2]. Here iris segmentation is done using semantic segmentation which is based on the U-Net architecture. The typical U-net architecture contains two paths- contracting path containing convolutional and pooling layers and the expanding path consists of transposed convolutional operations. The UBIRIS dataset is trained on the traditional U-Net model with some modifications according to the size of the images present in the UBIRIS dataset. The results obtained were very close to the ground truths and accuracy obtained is also appreciable.
Keywords: Iris Segmentation, Iris recognition system, Semantic segmentation, U-Net architecture, Convolutional layers, Pooling layers, Transposed Convolution layers.
Scope of the Article: Deep Learning