Multi-modal Iris Recognition System based on Convolution Neural Network
Gajanan Choudhari1, Rajesh Mehra2, Shallu3
1Gajanan Choudhari, Lecturer in Electronics at Government Women Residence Women Polytechnic Tasgaon , Maharashtra, India.
2Rajesh Mehra, Head of Curriculum Development Center at National Institute of Technical Teacher Training & Research, Chandigarh, India.
3Shallu, Development Center at National Institute of Technical Teacher Training & Research, Chandigarh, India.
Manuscript received on 06 July 2019 | Revised Manuscript received on 09 July 2019 | Manuscript published on 30 August 2019 | PP: 798-803 | Volume-8 Issue-10, August 2019 | Retrieval Number: J89110881019/2019©BEIESP | DOI: 10.35940/ijitee.J8911.0881019
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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: Iris is most promising bio-metric trait for identification or authentication. Iris consists of patterns that are unique and highly random in nature .The discriminative property of iris pattern has attracted many researchers attention. The unimodal system, which uses only one bio-metric trait, suffers from limitation such as inter-class variation, intra-class variation and non-universality. The multi-modal bio-metric system has ability to overcome these drawbacks by fusing multiple biometric traits. In this paper, a multi-modal iris recognition system is proposed. The features are extracted using convolutional neural network and softmax classifier is used for multi-class classification. Finally, rank level fusion method is used to fuse right and left iris in order to improve the confidence level of identification. This method is tested on two data sets namely IITD and CASIA-Iris-V3.
Keywords: Convolutional Neural Network, Softmax classifier, Iris Recognition, Bio-metric
Scope of the Article: Pattern Recognition and Analysis