IJBTT

Detection of Airport Disguise and Threat Objects using Shortwave-Infrared Imaging and Machine Learning Techniques

Detection of Airport Disguise and Threat Objects using Shortwave-Infrared Imaging and Machine Learning Techniques
	© 2022 by IJETT Journal
	Volume-70 Issue-2
	Year of Publication : 2022
	Authors : D. Sobya, Bhargav H Prakash, S. Nallusamy
	DOI :  10.14445/22315381/IJETT-V70I2P232

How to Cite?

D. Sobya, Bhargav H Prakash, S. Nallusamy, "Detection of Airport Disguise and Threat Objects using Shortwave-Infrared Imaging and Machine Learning Techniques," International Journal of Engineering Trends and Technology, vol. 70, no. 3, pp. 284-294, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I2P232

Abstract
Aiming to solve a problem that involves humans disguised with fake facial hair and skin at airport security inspection. By X-ray imaging for scanning of luggage to prevent security threats such as threat object detection. With the help of a Short wave-Infrared (SWIR) imaging camera, it can be detected whether a person is in disguise or not. As SWIR has 1-2 ?m of spectral range, it can differentiate the real facial hair and skin colour between fake facial hair and skin colour in the image. The luggage is the most vulnerable place where threat objects could be hidden inside any amount of luggage. X-ray imaging technology is most suitable for scanning because every material has a different degree of absorption of X-ray, and different items scatter the X-ray differently by which different colours could be obtained. Machine learning will be implemented to these images by which the detection of unwanted objects is identified in a possibly effective way. An automatic alarm will be sent to any defence system available to oversee the detected object and make way for further investigation. In contrast, the SWIR system is used to detect and disguise the person being investigated by the authorities on the spot. The threat object detection could be done by implementing specialized libraries in machine learning like Keras with Tensor Flow, OpenCV and Convolution Neural Network. With the combination of the above technologies, disguise detection and threat objects could be covered, which makes the airport security more secure and less vulnerable to any kinds of attacks.

Keywords
Face Disguise Detection, Threat Object Detection, Shortwave-Infrared, Machine Learning, Airport Security.

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