Evil and Fear Detection Using Facial Recognition Algorithms for Crime Prevention - A Survey

International Journal of Electrical and Electronics Engineering

© 2024 by SSRG - IJEEE Journal

Volume 11 Issue 5

Year of Publication : 2024

Authors : Ahmad Asnawi Ahmad Shukri, Lokman Mohd Fadzil

10.14445/23488379/IJEEE-V11I5P109

How to Cite?

Ahmad Asnawi Ahmad Shukri, Lokman Mohd Fadzil, "Evil and Fear Detection Using Facial Recognition Algorithms for Crime Prevention - A Survey," SSRG International Journal of Electrical and Electronics Engineering, vol. 11,  no. 5, pp. 85-101, 2024. Crossref, https://doi.org/10.14445/23488379/IJEEE-V11I5P109

Abstract:

The development of facial recognition technology has transformed a number of industries, including crime prevention and law enforcement. This work proposes a revolutionary method for improving crime prevention through the creation of a facial recognition system that can identify expressions linked to fear and malevolent intent. A model is being proposed to recognize minor facial cues suggestive of evil intentions and terror responses by utilizing deep learning techniques and a large dataset. With the use of face cues, including micro-expressions, eye movements, and muscular contractions, the proposed system is able to discriminate between people who are exhibiting such emotions. The system can be used for a wide range of purposes, such as real-time surveillance in public areas and witness and suspect identification to support investigations. However, when using such technology, privacy, ethics, and the possibility of prejudice issues need to be properly considered. The development, validation, and possible societal ramifications of the algorithm are covered in this paper, with a focus on the necessity of finding a balance between technical advancement and defending individual liberties and rights. The research aims to further the ongoing discussion on the appropriate application of facial recognition technology in public safety and crime prevention.

Keywords:

Crime prevention, Deep learning algorithm, Evil detection, Facial expression analysis, Facial recognition, Fear detection.

References:

[1] Ultralytics, GitHub. [Online]. Available: https://github.com/ultralytics/yolov5
[2] J.D., and J.A.B. Smith, “The Evolution of Human Detection Systems: From Early Concepts to Advanced Technologies,” Journal of Advanced Technology and Innovation, pp. 112-128, 2020.
[3] V.A. City, The Famous Antwerp Diamond Heist!. [Online]. Available: https://www.visitantwerpcity.com/antwerp-diamond-heist.html
[4] The Theft, Isabella Stewart Gardner Museum. [Online]. Available: https://www.gardnermuseum.org/about/theft-story
[5] Lawrence Lease, The Banco Central Burglary: A Daring Heist That Shocked the World, HubPages, 2023. [Online]. Available: https://discover.hubpages.com/politics/The-Banco-Central-Burglary-A-Daring-Heist-That-Shocked-the-World
[6] The Graff Diamond Heist : One of The Most Expensive in London’s History, The Natural Sapphire Company, Sapphires, 2015.  
[7] P. Malagarriga, The Robbery in 1981 at the ‘Banco Central’ and its Connection with Hotel Continentalel Atraco Al Banco Central de 1981 Y Su Vinculación Con Hotel Continental, Hotel Continental, Barcelona, 2023. [Online]. Available: https://blog.hotelcontinental.com/the-robbery-at-banco-central-in-1981-connection-with-hotel-continental/
[8] The Millennium Dome Heist, Crime + Investigation. [Online]. Available: https://www.crimeandinvestigation.co.uk/crimefiles/millennium-dome-heist
[9] Sophia S. Pasalis, The British Museum: A Rogue Curator and a Long History of Theft, The Harvard Crimson, 2023. [Online]. Available: https://www.thecrimson.com/article/2023/11/16/british-museum-rogue-curator-theft-artifacts-digital-collection/
[10] BMS Controls Articles, The Benefits of Access Control In Facilities Management: Enhancing Security, [Online]. Available: https://bmscontrols.co.uk/blog/the-benefits-of-access-control-in-facilities-management-enhancing-security/
[11] J. Redmon et al., “You Only Look Once: Unified, Real-Time Object Detection,” 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), USA, pp. 779-788, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Jonathan Hui, SSD Object Detection: Single Shot Multibox Detector for Real-Time Processing, 2018. [Online]. Available: https://jonathan-hui.medium.com/ssd-object-detection-single-shot-multibox-detector-for-real-time-processing-9bd8deac0e06
[13] 10 Main Types of Smiles and What They Really Mean, Healthline, 2019. [Online]. Available: https://www.healthline.com/health/typesof-smiles
[14] American Psychological Association, Publication manual of the American Psychological Association (7th ed.), APA PsycNet, 2020. [Online]. Available: https://psycnet.apa.org/record/2019-59141-000
[15] Marcus Andrews, 10 Types of Anger: What’s Your Anger style?, 2002. [Online]. Available: https://lifesupportscounselling.com.au/resources/blogs/10-types-of-anger-what-s-your-anger-style/
[16] Common and Unique Phobias Explained, Healthline. [Online]. Available: https://www.healthline.com/health/list-of-phobias