Manvi Khatri* and Ajay Sharma
| 1. Khatri, M. and Sharma, A. Application of Machine Learning Algorithms to Biometric Systems—The Traits Based Performance Analytic Survey. In International conference on smart computing and cyber security: strategic foresight, security challenges and innovation (pp. 253-267). Singapore: Springer Nature Singapore, 2021, June.
2. Bouzouina, Y. and Hamami, L. Multimodal biometric: Iris and face recognition based on feature selection of iris with GA and scores level fusion with SVM. In2017 2nd International Conference on Bio-engineering for Smart Technologies (BioSMART), IEEE, pp. 1-7, 2017, August.
3. Hezil, N. and Boukrouche, A.Multimodal biometric recognition using human ear and palmprint. IET Biom, vol. 6, no. 5, pp. 351-359, 2017
4. Chanukya P. S.V. V. N., and Thivakaran, T.K. Multimodal biometric cryptosystem for human authentication using fingerprint and ear. Multimedia Tools and Applications, vol. 79, no. 1, pp. 659-673. 2019
5. Ammour B., Boubchir L., Bouden T., andRamdani M.Face-Iris Multimodal Biometric Identification System, Electronics, vol. 9, no. 1, pp. 85. 2020
6. Ding, C. and Tao, D.Robust Face Recognition via Multimodal Deep Face Representation. IEEE Trans Multimedia, vol. 17, no. 11, pp. 2049-2058, 2015
7. Al-Waisy, A. S., Qahwaji, R., Ipson, S., Al-Fahdawi, S., and Nagem, T. A. M. A multi-biometric iris recognition system based on a deep learning approach. Pattern Analysis and Applications, vol. 21, no. 3, pp. 783-802, 2018
8. Al-Waisy A. S., Qahwaji R., Ipson S., andAl-Fahdawi, S, A multimodal biometric system for personal identification based on deep learning approaches. In Proceedings -2017 7th International Conference on Emerging Security Technologies, EST pp. 163-168, 2017
9. Soleymani S., Dabouei A., Kazemi H., Dawson J., andNasrabadi, N.M, Multi-Level Feature Abstraction from Convolutional Neural Networks for Multimodal Biometric Identification. In Proceedings of the2018 24th International Conference on Pattern Recognition, Beijing, China, pp. 3469-3476, 2018, August
10. Soleymani S., Torfi A., Dawson J. and Nasrabadi, N.M. Generalized bilinear deep convolutional neural networks for multimodal biometric identification. In2018 25th IEEE International Conference on Image Processing (ICIP), IEEE, pp. 763-767, 2018, October
11. Chollet F.Deep learning with Python. Simon and Schuster, 2021.
12. Nguyen K., Fookes C., Ross A., andSridharan S,Iris Recognition with Off-the-Shelf CNN Features: A Deep Learning Perspective, IEEE Access, vol. 6, pp. 18848-18855, 2017
13. Veluchamy, S.,Karlmarx, L. R.System for multimodal biometric recognition based on finger knuckle and finger vein using feature-level fusion and k-support vector machine classifier. IET Biom, vol. 6, no. 3, pp. 232-242, 2017
14. Gunasekaran K., Raja J., andPitchai R.Deep multimodal biometric recognition using contourlet derivative weighted rank fusion with human face, fingerprint and iris images. vol.60, no. 3, pp. 253-265. 2019
15. Panasiuk P., Szymkowski M., Dąbrowski M. and Saeed K., A multimodal biometric user identification system based on keystroke dynamics and mouse movements. In Computer Information Systems and Industrial Management: 15th IFIP TC8 International Conference, CISIM 2016, Vilnius, Lithuania, September 14-16, 2016, Proceedings 15, Springer International Publishing, pp. 672-681, 2016.
16. Liu W., Li W., Sun L., Zhang L., andChen P. Finger vein recognition based on deep learning. In Proceedings of the2017 12th IEEE Conference on Industrial Electronics and Applications, ICIEA 2017, vol. 2018, pp. 205-210, 2018, February
17. Jain A. K., Ross A. A., andNandakumar K, Introduction to Biometrics, 2011
18. Zhang D., Guo Z. and Gong Y., 2015. Multispectral biometrics: systems and applications. Springer.
19. Svoboda, J., Masci, J. and Bronstein, M.M. Palmprint recognition via discriminative index learning. In2016 23rd International Conference on Pattern Recognition (ICPR), IEEE, pp. 4232-4237, 2016, December.
20. Arora G., Kalra S., Bhatia A. and Tiwari K.Palmhashnet: Palmprint hashing network for indexing large databases to boost identification. IEEE Access, vol. 9, pp.145912-145928, 2021.
21. Guo L. M.S. Deep learning for visual understanding: A review. Neurocomputing, pp. 27-48, 2016
22. Chollet F.Deep learning with Python. Simon and Schuster, 2021.
23. Colaboratory, https://colab.research.google.com/, accessed in July 2023.
24. Chollet F.Keras Documentation, https://keras.io, accessed in July 2023.
25. Kumar A.Toward more accurate matching of contactless palmprint images under less constrained environments. IEEE Transactions on Information Forensics and Security, vol. 14, no. 1, pp. 34-47, 2018
26. Pattabhi Ramaiah, N. and Kumar, A.Towards more accurate iris recognition using bi-spectral imaging and cross-spectral matching capability, IEEE Transactions on Image Processing, vol. 26, pp. 208-221, 2017.
27. Wang, K. and Kumar, A.Cross-spectral iris recognition using CNN and supervised discrete hashing, Pattern Recognition, vol. 86, pp. 85-98, 2019
28. Panetta K., Wan Q., Agaian S., Rajeev S., Kamath S., Rajendran R., Rao S. P., Kaszowska A., Taylor H. A., Samani A., andYuan X,A comprehensive database for benchmarking imaging systems, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 42, no. 3, pp. 509-520, 2018
|||Shreshtha Singh and Arun Sharma. State of the Art Convolutional Neural Networks [J]. Int J Performability Eng, 2023, 19(5): 342-349.|
|||Vaishali Arya and Tapas Kumar. Boosting X-Ray Scans Feature for Enriched Diagnosis of Pediatric Pneumonia using Deep Learning Models [J]. Int J Performability Eng, 2023, 19(3): 175-183.|
|||Shikha Choudhary and Bhawna Saxena. Image-Based Crop Disease Detection using Machine Learning Approaches: A Survey [J]. Int J Performability Eng, 2023, 19(2): 122-132.|
|||Mansi Mahendru and Sanjay Kumar Dubey. Portable Learning Approach towards Capturing Social Intimidating Activities using Big Data and Deep Learning Technologies [J]. Int J Performability Eng, 2022, 18(9): 668-678.|
|||Sandhya Alagarsamy and Visumathi James. RNN LSTM-based Deep Hybrid Learning Model for Text Classification using Machine Learning Variant XGBoost [J]. Int J Performability Eng, 2022, 18(8): 545-551.|
|||Keshav H. Jatakar, Gopal Mulgund, Abhishek D. Patange, B. B. Deshmukh, and Kishor S. Rambhad. Multi-Point Face Milling Tool Condition Monitoring Through Vibration Spectrogram and LSTM-Autoencoder [J]. Int J Performability Eng, 2022, 18(8): 570-579.|
|||K. Lavanya, Smrithi Prakash, Yash Gedam, Altamash Aijaz, and L. Ramanathan. Real Time Digital Face Mask Detection using MobileNet-V2 and SSD with Apache Spark [J]. Int J Performability Eng, 2022, 18(8): 598-604.|
|||Cheran Ratnam and Junhua Ding. Big Four Bank Performance on Facebook and Instagram: An Analysis of Post Engagement [J]. Int J Performability Eng, 2022, 18(7): 475-484.|
|||Rajan Prasad Tripathi, Sunil Kumar Khatri, and Darelle Van Greunen. Relative Examination of Breast Malignant Growth Analysis Utilizing Different Machine Learning Algorithms [J]. Int J Performability Eng, 2022, 18(6): 417-425.|
|||Poonam Narang, Ajay Vikram Singh, and Himanshu Monga. Hybrid Metaheuristic Approach for Detection of Fake News on Social Media [J]. Int J Performability Eng, 2022, 18(6): 434-443.|
|||Dan Lu and Shunkun Yang*. A Survey of the Analysis of Complex Systems based on Complex Network Theory and Deep Learning [J]. Int J Performability Eng, 2022, 18(4): 241-250.|
|||Sukruta Pardeshi, chetana Khairnar, and Khalid Alfatmi. Analysis of Data Handling Challenges in Edge Computing [J]. Int J Performability Eng, 2022, 18(3): 176-187.|
|||Geetanjali S. Mahamunkar, Arvind W. Kiwelekar, and Laxman D. Netak. Deep Learning Model for Black Spot Classification [J]. Int J Performability Eng, 2022, 18(3): 222-230.|
|||Mamta Bhamare, and K Ashokkumar. Personality Prediction through Social Media Posts [J]. Int J Performability Eng, 2022, 18(11): 817-825.|
|||Jibi G. Thanikkal, Ashwani Kumar Dubey, and Thomas M. T.. Deep Learning based Aquatic and Semi Aquatic Plants Morphological Features Extraction and Classification [J]. Int J Performability Eng, 2022, 18(10): 702-709.|