IRIS Recognition-Based Wheelchair for Quadriplegia Prone Persons

Authors

  • Anchal  Baba Mastnath University (BMU), Haryana, India
  • Dr. Priyanka Bansal  Baba Mastnath University (BMU), Haryana, India

DOI:

https://doi.org/10.32628/CSEIT239922

Keywords:

Quadriplegia, Wheelchair, Adaptive Learning, Iris

Abstract

This abstract presents the concept of an iris recognition-based wheelchair system designed specifically for individuals with quadriplegia, a condition that results in paralysis of all four limbs. The proposed wheelchair aims to enhance the mobility and independence of quadriplegia-prone individuals by utilizing iris recognition technology to facilitate seamless control of the wheelchair's movement. The system utilizes a non-intrusive iris recognition algorithm to accurately identify the user's iris patterns, which are unique to each individual. By integrating this technology into the wheelchair's control system, the user can navigate the wheelchair by simply focusing their gaze on specific directions or objects within their field of vision. The system captures real-time iris images and processes them using advanced computer vision techniques to determine the intended movement commands. Through this innovative approach, the wheelchair eliminates the need for physical contact or manual input devices, thereby offering a more natural and intuitive control mechanism for individuals with quadriplegia. The iris recognition-based control system not only enables precise navigation but also provides customizable features such as speed adjustment, obstacle detection, and path planning, ensuring a safe and efficient user experience. Furthermore, the proposed system incorporates machine learning algorithms to continuously improve the iris recognition accuracy and adapt to individual users' changing eye patterns over time. This adaptive learning capability enhances the system's reliability and ensures consistent and reliable control of the wheelchair.

References

  1. D. Yadav, N. Kohli, S. Yadav, M. Vatsa, R. Singh, and A. Noore, “Iris presentation attack via textured contact lens in unconstrained environment,” in Proceedings of the 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Tahoe, NV, USA, March 2018.
  2. Z. Wang, C. Li, H. Shao, and J. Sun, “Eye recognition with mixed convolutional and residual network (MiCoRe-Net),” IEEE Access, vol. 6, pp. 17905–17912, 2018.
  3. M. Liu, Z. Zhou, P. Shang, and D. Xu, “Fuzzified image enhancement for deep learning in iris recognition,” IEEE Transactions on Fuzzy Systems, vol. 28, no. 1, pp. 92–99, 2019.
  4. K. W. Bowyer, K. Hollingsworth, and P. J. Flynn, “Image understanding for iris biometrics: a survey,” Computer Vision and Image Understanding, vol. 110, no. 2, pp. 281–307, 2008.
  5. H. Proença and L. A. Alexandre, “Iris recognition: analysis of the error rates regarding the accuracy of the segmentation stage,” Image and Vision Computing, vol. 28, no. 1, pp. 202–206, 2010.
  6. Sumedh Kulkarni[1], Beena Ballal[2], Harshit Vaishya[3], Yash Mehta[4], Pranav Mokal[5]. "A novel approach for code match in iris recognition." In Computer and Information Science (ICIS), 2016 IEEE/ACIS 12th International Conference on, pp. 123-128. IEEE, 2017.
  7. AKM Bahalul Haque Shawan Shurid Afsana Tasnim Juha Md. Shadman Sadique Abu Sayem Mohammad Asaduzzaman "Video Sequence-Based Iris Recognition Inspired by Human Cognition Manner." Journal of Bionic Engineering 11, no. 3 (2020): 481-489. 
  8. Doshi Siddharth P. Shripad Deshpande "Iris recognition using combined support vector machine and Hamming distance approach." Expert Systems with Applications Vol 41, no. 2 (2016): 588-593. [54] Song, Yun, Wei Cao, and Zunliang He. "Robust iris recognition using sparse error correction model and discriminative dictionary learning." Neurocomputing137 (2016): 198-204. 
  9. Nutthanan Wanluk Aniwat Juhong Sarinporn Visitsattapongse C. Pintavirooj "Cross-Sensor Iris Recognition through Kernel Learning," Pattern Analysis and Machine Intelligence, IEEE Transactions on , vol.36, no.1, pp.73,85,Jan.2016 doi: 10.1109/TPAMI.2013.98
  10. Farwa Jafar, Syeda Farwa Fatima, Hafiza Ramsha Mushtaq, Sidra Khan, Amber Rasheed, Munazza Sadaf "Efficient and Accurate At-a-Distance Iris Recognition Using Geometric Key-Based Iris Encoding," Information Forensics and Security, IEEE Transactions on , vol.9, no.9, pp.1518,1526, Sept. 2019
  11. Reshma Abraham1, Ritta Maria Thaliath2, Swapna Davies, Thara Jacob3, Tintu Johny 4"Block based texture analysis for iris classification and matching." In Computer Vision and Pattern Recognition Workshops (CVPRW), 2020 IEEE Computer Society Conference on, pp. 30-37. IEEE, 2019.
  12. Jatin Sharma, Anbarasu M, Chandan Chakraborty and Shanmugasundaram M. "Iris based recognition system using wavelet transform." IJCSNS International Journal of Computer Science and Network Security 9, no. 11 (2017): 101-104. 
  13. M. VASANTHA LAKSHMI1, N.V.SRINIVAS2, M.LAKSHMINADH3, N. SATHISH KUMAR4, M. HAREESH TEJA5. "Eigenspace based accurate iris recognition system." InIndia Conference (INDICON), 2019 Annual IEEE, pp. 1-3. IEEE, 2020.
  14. Dianchun Bai ,Zhicheng Liu,Qing Hu,Junyou Yang,Guang Yang,Changlin Ni,Deguo Yang and Long Zhou, “Biometric Iris Recognition Based on Hybrid Technique,” Int. J. Soft Comput., vol. 2, no. 4, pp. 1–9, Nov. 2016. 

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2023-07-22

Issue

Volume 9, Issue 9, July-August-2023

Section

Research Articles

License

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Anchal, Dr. Priyanka Bansal, " IRIS Recognition-Based Wheelchair for Quadriplegia Prone Persons" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 9, Issue 9, pp.169-171, July-August-2023. Available at doi : https://doi.org/10.32628/CSEIT239922