Enhancing child safety with accurate fingerprint identification using deep learning technology

Seba Aziz Sahy, Yitong Niu, Ahmed L. Khalaf, Jamal Fadhil Tawfeq, Ahmed Dheyaa Radhi, Poh Soon JosephNg

Abstract


Utilizing deep learning algorithms to differentiate the fingerprints of children can greatly enhance their safety. This advanced technology enables precise identification of individual children, facilitating improved monitoring and tracking of their activities and movements. This can effectively prevent abductions and other forms of harm, while also providing a valuable resource for law enforcement and other organizations responsible for safeguarding children. Furthermore, the use of deep learning algorithms minimizes the potential for errors and enhances the overall accuracy of fingerprint recognition. Overall, implementing this technology has immense potential to significantly improve the safety of children in various settings. Our experiments have demonstrated that deep learning significantly enhances the accuracy of fingerprint recognition for children. The model accurately classified fingerprints with an overall accuracy rate of 93%, surpassing traditional fingerprint recognition techniques by a significant margin. Additionally, it correctly identified individual children's fingerprints with an accuracy rate of 89%, showcasing its ability to distinguish between different sets of fingerprints belonging to different children.

Full Text:

PDF

References


J. Vieira, E. Leitinger, M. Sarajlic, X. Li, and F. Tufvesson, “Deep convolutional neural networks for massive MIMO fingerprint-based positioning,” in 2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC, Oct. 2017, pp. 1–6.

M. G. Sarwar Murshed, K. Bahmani, S. Schuckers, and F. Hussain, “Deep Age-Invariant Fingerprint Segmentation System.” 2023.

K. Rajaram, A. Devi, and S. Selvakumar, “PalmNet: A CNN Transfer Learning Approach for Recognition of Young Children Using Contactless Palmprints,” in Machine Learning and Autonomous Systems: Proceedings of ICMLAS 2021, Singapore, 2022, pp. 609–622.

S. Siddiqui, M. Vatsa, and R. Singh, “Face Recognition for Newborns, Toddlers, and Pre-School Children: A Deep Learning Approach,” in 24th International Conference on Pattern Recognition (ICPR, Beijing, China, 2018, pp. 3156-3161,. doi: 10.1109/ICPR.2018.8545742.

V. N. Patil and D. R. Ingle, “A Novel Approach for ABO Blood Group Prediction using Fingerprint through Optimized Convolutional Neural Network,” International Journal of Intelligent Systems and Applications in Engineering, vol. 10, no. 1, pp. 60–68, 2022.

R. H. Lamia and E. B. A. Najoua, “Biometric authentication based on multi-instance fingerprint fusion in degraded context,” in 2019 16th International Multi-Conference on Systems, Signals & Devices (SSD, Mar. 2019, pp. 22–27.

S. Kim, “Learning a Convolutional Neural Network with Additional Information.” 2016.

R. F. Nogueira, R. Alencar Lotufo, and R. C. Machado, “Fingerprint liveness detection using convolutional neural networks,” IEEE transactions on information forensics and security, vol. 11, no. 6, pp. 1206–1213, 2016.

S. Minaee, E. Azimi, and A. Abdolrashidi, “Fingernet: Pushing the limits of fingerprint recognition using convolutional neural.” 2019. [Online]. Available: network. arXiv

V. Kamble, M. Dale, and V. Bairagi, “A Hybrid Model by Combining Discrete Cosine Transform and Deep Learning for Children Fingerprint Identification,” International Journal of Advanced Computer Science and Applications, vol. 14, no. 1, 2023.

V. Kamble and M. Dale, “Children biometric recognition for the betterment of society using deep intelligence.” 2022.

J. Li, J. Feng, C. C. J. Kuo, J. Li, J. Feng, and C. C. J. Kuo, “Deep convolutional neural network for latent fingerprint enhancement,” Signal Processing: Image Communication, vol. 60, pp. 52–63, 2018.

R. D. P. Lemes, O. R. P. Bellon, L. Silva, and A. K. Jain, “Biometric recognition of newborns: Identification using palmprints,” in 1st IEEE IJCB, 2011, pp. 1-6,.

M. Chhabra, K. K. Ravulakollu, M. Kumar, A. Sharma, and A. Nayyar, “Improving automated latent fingerprint detection and segmentation using deep convolutional neural network,” Neural Computing and Applications, vol. 35, no. 9, pp. 6471–6497, 2023.

K. L. Bar-Or and J. Almog, “Children and guns: The detection of recent contact with firearms on children’s hands by the PDT reagent,” Forensic science international, vol. 253, pp. 43–47, 2015.

H. E. Lee, T. Ermakova, V. Ververis, and B. Fabian, “Detecting child sexual abuse material: A comprehensive survey,” Forensic Science International: Digital Investigation, vol. 34, p. 301022, 2020.

J. Kotzerke, S. Davis, K. Horadam, and J. McVernon, “Newborn and infant footprint crease pattern extraction,” in 20th IEEE ICIP, 2013, pp. 4181-4185,.

A. K. Jain, A. A. Ross, and K. Nandakumar, Introduction to biometrics. Springer Science & Business Media, 2011.

A. K. Jain, K. Cao, and S. S. Arora, “Recognizing infants and toddlers using fingerprints: Increasing the vaccination coverage,” in IEEE International Joint Conference on Biometrics (IJCB, 2014, pp. 1-8,.

A. K. Jain, S. S. Arora, K. Cao, L. Best-Rowden, and A. Bhatnagar, “Fingerprint recognition of young children,” IEEE Transactions on Information Forensics and Security, vol. 12, no. 7, pp. 1501–1514, 2016.

P. Grother, J. Matey, E. Tabassi, G. Quinn, and M. C. IREX VI, “Temporal Stability of Iris Recognition Accuracy,” NIST Interagency Report, vol. 7948, 2013.

F. Galton, Finger prints of young children. British Association for the Advancement of Science, 1899.

P. M. Corby et al., “Using biometrics for participant identification in a research study: a case report,” J. Am. Medical Informatics Assoc, vol. 13, no. 2, pp. 233–235, 2006.

A. A. Al-Rababah, T. AlTamimi, and N. Shalash, “A New Model for Software Engineering Systems Quality Improvement,” Res. J. Appl. Sci. Eng. Technol., vol. 7, no. 13, pp. 2724–2728, Apr. 2014, doi: 10.19026/rjaset.7.592.

A. A. Al-Rababah and U. A. Al-Rababah, “Electric Voltage Control as an Implementation of Neural Network Applications,” J. Comput. Sci., vol. 4, no. 10, pp. 815–820, Oct. 2008, doi: 10.3844/jcssp.2008.815.820.

S. Q. Salih, A. A. Alsewari, and Z. M. Yaseen, “Pressure Vessel Design Simulation: Implementing of Multi-Swarm Particle Swarm Optimization,” Proc. 2019 8th Int. Conf. Softw. Comput. Appl., pp. 120–124, 2019, doi: 10.1145/3316615.3316643.

S. Q. Salih, “A New Training Method Based on Black Hole Algorithm for Convolutional Neural Network,” J. Sourthwest Jiaotong Univ., vol. 54, no. 3, pp. 1–10, 2019, doi: 10.1002/9783527678679.dg01121.

A. Malik et al., “Pan Evaporation Estimation in Uttarakhand and Uttar Pradesh States, India: Validity of an Integrative Data Intelligence Model,” Atmosphere (Basel)., vol. 11, no. 6, p. 553, May 2020, doi: 10.3390/atmos11060553.

H. Tao, S. M. Awadh, S. Q. Salih, S. S. Shafik, and Z. M. Yaseen, “Integration of extreme gradient boosting feature selection approach with machine learning models: application of weather relative humidity prediction,” Neural Comput. Appl., 2022, doi: 10.1007/s00521-021-06362-3.

A. Malik, A. Kumar, O. Kisi, N. Khan, S. Q. Salih, and Z. M. Yaseen, “Analysis of dry and wet climate characteristics at Uttarakhand (India) using effective drought index,” Nat. Hazards, 2021, doi: 10.1007/s11069-020-04370-5.

H. Tao et al., “Training and Testing Data Division Influence on Hybrid Machine Learning Model Process: Application of River Flow Forecasting,” Complexity, vol. 2020, pp. 1–22, Oct. 2020, doi: 10.1155/2020/8844367.

B. Karimi, P. Mohammadi, H. Sanikhani, S. Q. Salih, and Z. M. Yaseen, “Modeling wetted areas of moisture bulb for drip irrigation systems: An enhanced empirical model and artificial neural network,” Comput. Electron. Agric., 2020, doi: 10.1016/j.compag.2020.105767.

F. Cui, S. Q. Salih, B. Choubin, S. K. Bhagat, P. Samui, and Z. M. Yaseen, “Newly explored machine learning model for river flow time series forecasting at Mary River, Australia,” Environ. Monit. Assess., 2020, doi: 10.1007/s10661-020-08724-1.

T. Hai et al., “DependData: Data collection dependability through three-layer decision-making in BSNs for healthcare monitoring,” Inf. Fusion, vol. 62, pp. 32–46, Oct. 2020, doi: 10.1016/j.inffus.2020.03.004.

S. Q. Salih, M. Habib, I. Aljarah, H. Faris, and Z. M. Yaseen, “An evolutionary optimized artificial intelligence model for modeling scouring depth of submerged weir,” Eng. Appl. Artif. Intell., vol. 96, p. 104012, Nov. 2020, doi: 10.1016/j.engappai.2020.104012

S. A. M. Al-Juboori, F. Hazzaa, S. Salih, Z. S. Jabbar, and H. M. Gheni, “Man-in-the-middle and denial of service attacks detection using machine learning algorithms,” Bull. Electr. Eng. Informatics, vol. 12, no. 1, pp. 418–426, Feb. 2023, doi: 10.11591/eei.v12i1.4555.

A. S. Shibghatullah and I. Al Barazanchi, “An Analysis of the Requirements for Efficient Protocols in WBAN,” J. Telecommun. Electron. Comput. Eng., vol. 6, no. 2, pp. 19–22, 2014.

Z. A. Jaaz, S. S. Oleiwi, S. A. Sahy, and I. Albarazanchi, “Database techniques for resilient network monitoring and inspection,” TELKOMNIKA Telecommun. Comput. Electron. Control, vol. 18, no. 5, pp. 2412–2420, 2020, doi: 10.12928/TELKOMNIKA.v18i5.14305.

I. Al Barazanchi, H. R. Abdulshaheed, and A. Shibghatullah, “The Communication Technologies in WBAN,” Int. J. Adv. Sci. Technol., vol. 28, no. 8, pp. 543–549, 2019.

I. Al Barazanchi, A. Murthy, A. Abdulqadir, A. Rababah, and G. Khader, “Blockchain Technology - Based Solutions for IOT Security,” Iraqi J. Comput. Sci. Math., vol. 3, no. 1, pp. 1–12, 2022.

Z. A. Jaaz, M. E. Rusli, N. A. Rahmat, I. Y. Khudhair, I. Al Barazanchi, and H. S. Mehdy, “A Review on Energy-Efficient Smart Home Load Forecasting Techniques,” Int. Conf. Electr. Eng. Comput. Sci. Informatics, vol. 2021-Octob, no. October, pp. 233–240, 2021, doi: 10.23919/EECSI53397.2021.9624274.

A. S. Shibghatullah and I. Al Barazanchi, “An Analysis of the Requirements for Efficient Protocols in WBAN,” J. Telecommun. Electron. Comput. Eng., vol. 6, no. 2, pp. 19–22, 2014.




DOI: http://dx.doi.org/10.21533/pen.v11i3.3625

Refbacks

  • There are currently no refbacks.


Copyright (c) 2023 Seba Aziz Sahy, Yitong Niu, Ahmed L. Khalaf, Jamal Fadhil Tawfeq, Ahmed Dheyaa Radhi, Poh Soon JosephNg

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

ISSN: 2303-4521

Digital Object Identifier DOI: 10.21533/pen

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