ICTACT Journal on Image and Video Processing

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

IRIS Detection For Biometric Pattern Identification Using Deep Learning


Affiliations
1 Department of Computer Science and Engineering, HKBK College of Engineering, India
2 Department of Computer Science and Engineering, Presidency University, India
3 Department of Computer Science, The Quaide Milleth College for Men, India
4 Department of Electronics and Telecommunications Engineering, University of Technology and Applied Sciences, Oman
     

   Subscribe/Renew Journal


In this paper, we develop a liveness detection of iris present in the study to reduce various spoofing attacks using gray-level co-occurrence matrix (GLCM) and Deep Learning (DL). The input images of iris are been given to this technique for the extraction of texture and colour features with fine details. The details are fused finally and given to a DL classifier for the classification of liveness detection. The simulation is conducted to test the efficacy of the model and the results of simulation shows that the proposed method achieves higher level of accuracy than existing methods.

Keywords

Iris Detection, Pattern Identification, Liveness Detection, Biometric, Deep Learning
Subscription Login to verify subscription
User
Notifications
Font Size

  • Z. Zhao and A. Kumar, “A Deep Learning based Unified Framework to Detect, Segment and Recognize Irises using Spatially Corresponding Features”, Pattern Recognition, Vol. 93, pp. 546-557, 2019.

  • S. Karthick and P.A. Rajakumari, “Ensemble Similarity Clustering Frame work for Categorical Dataset Clustering Using Swarm Intelligence”, Proceedings of International Conference on Intelligent Computing and Applications, pp. 549-557, 2021.

  • A. Khadidos, A.O. Khadidos and S. Kannan, “Analysis of COVID-19 Infections on a CT Image using Deep Sense Model”, Frontiers in Public Health, Vol. 8, pp. 1-18, 2020.

  • K. Srihari, G. Dhiman and S. Chandragandhi, “An IoT and Machine Learning‐based Routing Protocol for Reconfigurable Engineering Application”, IET Communications, Vol. 23, No. 2, pp. 1-15, 2021.

  • S.B. Sangeetha, R. Sabitha and B. Dhiyanesh, “Resource Management Framework using Deep Neural Networks in Multi-Cloud Environment”, Proceedings of International Conference on Operationalizing Multi-Cloud Environments, pp. 89-104, 2021.

  • H. Proenca and J.C. Neves, “Deep-Prwis: Periocular Recognition without the Iris and Sclera using Deep Learning Frameworks”, IEEE Transactions on Information Forensics and Security, Vol. 13, No. 4, pp. 888-896, 2017.

  • H. Proenca and J.C. Neves, “Segmentation-Less and NonHolistic Deep-Learning Frameworks for Iris Recognition”, Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 1-8, 2019.

  • N.V. Kousik and M. Saravanan, “A Review of Various Reversible Embedding Mechanisms”, International Journal of Intelligence and Sustainable Computing, Vol. 1, No. 3, pp. 233-266, 2021.

  • I.J. Jacob, “Capsule Network based Biometric Recognition System”, Journal of Artificial Intelligence, Vol. 1, No. 2, pp. 83-94, 2019.

  • M. Vatsa, R. Singh and A. Majumdar, “Deep Learning in Biometrics”, CRC Press, 2018.

  • V. Maheshwari, M.R. Mahmood, S. Sravanthi and N. Arivazhagan, “Nanotechnology-Based Sensitive Biosensors for COVID-19 Prediction Using Fuzzy Logic Control”, Journal of Nanomaterials, Vol. 2021, pp. 1-14, 2021.

  • S. Umer, A. Sardar and B.C. Dhara, “Person Identification using Fusion of Iris and Periocular Deep Features”, Neural Networks, Vol. 122, pp. 407-419, 2020.

  • S. Arora and M.P.S. Bhatia, “Presentation Attack Detection for Iris Recognition using Deep Learning”, International Journal of System Assurance Engineering and Management, Vol. 8, No. 2, pp. 1-7, 2020.


Abstract Views: 174

PDF Views: 0




  • IRIS Detection For Biometric Pattern Identification Using Deep Learning

Abstract Views: 174  |  PDF Views: 0

Authors

M. Ramkumar
Department of Computer Science and Engineering, HKBK College of Engineering, India
Amirtha Preeya
Department of Computer Science and Engineering, Presidency University, India
R. Manikandan
Department of Computer Science, The Quaide Milleth College for Men, India
T. Karthikeyan
Department of Electronics and Telecommunications Engineering, University of Technology and Applied Sciences, Oman

Abstract


In this paper, we develop a liveness detection of iris present in the study to reduce various spoofing attacks using gray-level co-occurrence matrix (GLCM) and Deep Learning (DL). The input images of iris are been given to this technique for the extraction of texture and colour features with fine details. The details are fused finally and given to a DL classifier for the classification of liveness detection. The simulation is conducted to test the efficacy of the model and the results of simulation shows that the proposed method achieves higher level of accuracy than existing methods.

Keywords


Iris Detection, Pattern Identification, Liveness Detection, Biometric, Deep Learning

References