Infrared Radiation Enabled Segmentation And Forensics
Keywords:
suspiciousness, weapon detection, digital forensics, neural networks, convolutional neural network (CNN)
Abstract
This paper discusses the approach that discovers the suspiciousness of a suspected person and also perceives
whether the suspicious person has concealed (hidden) firearm or not as well as evaluates its accuracy with the testimony of
suspiciousness from IR image accurately and efficiently in order to save time in the confirmation of suspect while breaking
down the barrier with the assurance by spotting the hidden weapon under suspected suspicious person’s clothes with
supervised machine learning, 2D convolutional neural network approach with a 3-by-3 layer for the intention of the detection
of weapon and suspicious assessment measured under Digital Forensics
References
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Scalable Inf. Syst., p. 169418, 2018, DOI: 10.4108/eai.21-4-2021.169418.
[2] F. A. A. A. A. KHAN, A. BURDI, S. AWAN, H. A. SHAH, “Image Segmentation Approach Using Python OpenCV
to Detect Tuberculosis,” SINDH Univ. J. (SCIENCE Ser. Image, vol. 51, no. 01, pp. 135–140, 2019.
[3] A. A. Khan et al., “IMG-forensics: Multimedia-enabled information hiding investigation using convolutional neural
network,” IET Image Process., no. February, pp. 1–9, 2021, DOI: 10.1049/ipr2.12272.
[4] A Khan, A Laghari,” Digital Forensics and Cyber Forensics Investigation: Security Challenges, Limitations, Open
Issues, and Future Direction”. IJES and Digital Forensics (2021).. 10.1504/ijesdf.2022.10037882.
[5] Q. Wang, C. Yuan, and Y. Liu, “Learning Deep Conditional Neural Network for Image Segmentation,” IEEE Trans.
Multimed., vol. 21, no. 7, pp. 1839–1852, 2019, DOI: 10.1109/TMM.2018.2890360.
[6] C.-F. Juang, C.-M. Chang, J.-R. Wu, and D. Lee, “Computer Vision-Based Human Body Segmentation and Posture
Estimation,” IEEE Trans. Syst. Man, Cybern. - Part A Syst. Humans, vol. 39, no. 1, pp. 119–133, 2008, DOI:
10.1109/tsmca.2008.2008397.
Published
2022-08-15
How to Cite
Khatri, W., Ali, W., Romaan, R., Ayub, A., & Ahmed, Z. (2022). Infrared Radiation Enabled Segmentation And Forensics. International Journal of Computing and Related Technologies, 3(No 1), 24-29. Retrieved from http://ijcrt.smiu.edu.pk/ijcrt/index.php/smiu/article/view/128
Section
Articles
