International Research Journal of Innovations in Engineering and Technology - IRJIET International Open Access, Monthly, Peer Reviewed, Reputed Journal ISSN (online): 2581-3048

Impact Factor (2025): 6.9

DOI Prefix: 10.47001/IRJIET

Crime Suspect Identification System Using Deep Neural Vision Processing Technique

Orishedere SundayDepartment of Computer Science and Mathematics, Novena University, Ogume, NigeriaLinus PeterCenter for Information and Telecommunication Engineering, University of Port Harcourt, NigeriaIsizoh A.N.Department of Electronic and Computer Engineering, Nnamdi Azikiwe University, Awka, Nigeria

Vol 7 No 4 (2023): Volume 7, Issue 4, April 2023 | Pages: 188-201

International Research Journal of Innovations in Engineering and Technology

OPEN ACCESS | Research Article | Published Date: 03-05-2023

doi Logo doi.org/10.47001/IRJIET/2023.704029

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Abstract

This research discusses the improvement of facial recognition system in crime suspect identification system using deep neural vision processing technique. The research was motivated based on the problem of identification of crime suspect which security agents do experience these days. Several models like discrete wavelet transform model, rendering model, convolutional neural network model were used to design the new system which utilizes enhanced facial recognition approach and implemented on a Mathlab environment. The system was tested and validated using tenfold cross validation technique and the accuracy achieved was 99.22% which is very good. The system was later deployed at the Nigerian Police Force and tested for reliability using various facial expressions of volunteered criminals; and the result was excellent.

Keywords

Facial recognition, deep neural vision, biometrics, and rendering model


Citation of this Article

Orishedere Sunday, Linus Peter, Isizoh A.N., “Crime Suspect Identification System Using Deep Neural Vision Processing Technique” Published in International Research Journal of Innovations in Engineering and Technology - IRJIET, Volume 7, Issue 4, pp 188-201, April 2023. Article DOI https://doi.org/10.47001/IRJIET/2023.704029

Licence Copyright (c) 2026 International Research Journal of Innovations in Engineering and Technology creative common licence This work is licensed under Creative common Attribution Non Commercial 4.0 Internation Licence
References
  1. Ana Betrand (2012) “Face localization and facial feature extraction based on shape and color segmentation”. 16th IEEE international conference on pattern recognition; Volume 2, Issue 2, ISSN 2091-2730.
  2. Andrew Enughwure (2015); “A Combination Approach to Face Recognition, “Bishop University. Volume 1, Issue 4 46(2):235–245.
  3. Arun Alvappillai and Peter Neal Barrina (2018), “Face Recognition using Machine Learning”, IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
  4. Calister Obi (2015) “Challenges of Insecurity and Terrorism in Nigeria: Implication for National Development” OIDA International Journal of Sustainable Development, Vol. 08, No. 02, pp. 11-18, 8 Pages Posted: 29.
  5. Girshick, J. Donahue, T. Darrell, and J. Malik (2014) “Rich feature hierarchies for accurate object detection and semantic segmentation”. In CVPR.
  6. Haider Mehraj and Ajaz Hussain Mir (2020) “A Survey of Biometric Recognition Using Deep Learning”, EAI Endorsed Transactions on Energy Web 03 2021 – 05; Volume 8; Issue 33.
  7. Karina Enriquez (2018) “Faster Face Detection using Convolutional Neural Networks & the Viola-Jones Algorithm”, California State University Stanislaus, 1 University Circle, Turlock, CA 95382.
  8. Levine, S., Finn, C., Darrell, T. &Abbeel, P. (2016). “End-to-end training of deep visuomotor policies,” J. Mach. Learning Res., vol. 17, no. 39, pp. 1–40.
  9. Lin S.D, Chen K. (2019) “Illumination Invariant Thermal Face Recognition using Convolutional Neural Network”, IEEE International Conference on Consumer Electronics -Asia (ICCE-Asia).
  10. Liu X, Guo Z, You J, Kumar B. (2020) “Dependency-Aware Attention Control for Image Set-Based Face Recognition”. IEEE Transactions on Information Forensics and Security.
  11. Mohd B., Sandeep S., Nelson K (2012), “Survey on Noise Removal in Digital Images” IOSRJCE; ISSN 2278-0661; vol 6; Issue 4; PP 45-51.
  12. Prajyot H. Mohite, Pratibha P. Shingare (2020), “Deep Learning Approach For Facial Expression Recognition”, Journal of critical reviews; ISSN- 2394-5125 Vol 7, Issue 14.
  13. Russell, S. and Norvig, P. (2010). “Artificial Intelligence: A Modern Approach”, 3rd Edition. New Jersey, Prentice Hall.
  14. Vimal, Virender Kadyan (2015) “Implementation and Performance Analysis of Face Recognition Using MATLAB”. IJCST Vol. 6, Issue 2, ISSN: 0976-8491 (Online) | ISSN : 2229-4333.
  15. Wei T.  Jing X. Y., H. S. Wong, D. Zhang, (April 2006.), “Face recognition based on 2D Fisher face approach, Pattern Recognition”, 39, 4, pp. 707–710.
  16. Yogita Mahajan, Shanta Sondur (2018), “Aging face recognition using deep learning”, International Journal of Engineering and Applied Sciences (IJEAS) ISSN: 2394-3661, Volume-5, Issue-8.
  17. Zeenathunisa S, Jaya A., Rabbani M., (2011) “A biometric approach towards recognizing face in various dark illumination”, IEEE, PP1-7.

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