Iris Recognition using Convolutional Neural Network Design
Gajanan Choudhari1, Rajesh Mehra2
1Gajanan Choudhari, Department of ECE, National Institute of Technical Teachers Training and Research Chandigarh, India.
2Dr. Rajesh Mehra, Department of ECE, National Institute of Technical Teachers Training and Research Chandigarh, India.
Manuscript received on 05 August 2019 | Revised Manuscript received on 12 August 2019 | Manuscript Published on 26 August 2019 | PP: 672-678 | Volume-8 Issue-9S August 2019 | Retrieval Number: I11080789S19/19©BEIESP | DOI: 10.35940/ijitee.I1108.0789S19
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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 trait has gained the attention of many researchers recently as it consists of unique and highly random patterns. Many methods have been proposed for feature extraction and classification for iris trait but suffer from poor generalization ability. In this paper, a scratch convolutional neural network is designed in order to extract the iris features and softmax classifier is used for multiclass classification. The various optimization techniques with backpropagation algorithm are used for weight updating. The results show that the Convolutional Neural Network based feature extraction has proven to provide good generalization ability with improved recognition rate. The effect of various optimization techniques for generalization ability is also observed. The method is tested on IITD and CASIA-Iris-V3 database. The recognition rates obtained are comparable with state of art methods.
Keywords: Bio-metric, Deep Learning, Iris Recognition, Softmax Classifier, Adam, SGD with Moment, RMSprop, Convolutional Neural Network.
Scope of the Article: Machine/ Deep Learning with IoT & IoE