Detection of Airport Disguise and Threat Objects using Shortwave-Infrared Imaging and Machine Learning Techniques
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
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.
Face Disguise Detection, Threat Object Detection, Shortwave-Infrared, Machine Learning, Airport Security.
 Steve Karoly, Technologies to counter aviation security threats, AIP Conference Proceedings. 1898 (1) (2017) 01-09.
 Ruwantissa Abeyratne, The ePassport - new technology to counter security threats, Journal of Transportation Security. 6(1) (2013 27-42.
 JulianJang-Jaccard, A survey of emerging threats in cybersecurity, Journal of Computer and System Sciences. 80(5) (2014) 973-993.
 Kruti Goyal, Face detection and tracking using OpenCV, International Conference on Electronics, Communication and Aerospace Technology. (2017) 26-34.
 Kumbhar et al., Real-time face detection and tracking using OpenCV, International Journal for Research in Emerging Science and Technology. 4(4) (2017) 39-43.
 Paul Viola, Jeffrey and Jones Robust, Real-time object detection, International Journal of Computer Vision. 57 (2001) 137-154
 Sanjeev Sharma et al., Face detection using combined skin colour detector and template matching method, International Journal of Computer Applications. 26(7) (2011) 0975-0987.
 Mahmudul Hasan Robin et al., Improvement of the face and eye detection performance by using multitask cascaded convolutional networks, IEEE Region 10 Symposium (TENSYMP). (2020) 5-7.
 Zhang, Zhang, Li and Qiao, Joint face detection and alignment using multitask cascaded convolutional networks, IEEE Signal Processing Letters. 23(10) (2016) 1499-1503.
 Sarala A. Dabhade and Mrunal S. Bewoor, Real-time face detection and recognition using HAAR - based cascade classifier and principal component analysis, International Journal of Computer Science and Management Research. 1(1) (2012) 94-99.
 Zhang et al., Machine learning and visual computing, Applied Computational Intelligence and Soft Computing. 2017 (2017), 01-01.
 Dan-Ioan Gota et al., Threat objects detection in the airport using machine learning, International Carpathian Control Conference. (2020) 27-29.
 Suresh Kumar, Thangamani, Sasikumar, and Nallusamy, An improved machine learning approach for predicting ischemic stroke, International Journal of Engineering Trends and Technology. 69(1), (2021) 111-115.
 Ponti et al., Image restoration using gradient iteration and constraints for band extrapolation, IEEE Journal of Selected Topics in Signal Processing. 10(1) (2016) 71-80.
 Qiang Gao et al., An X-ray image enhancement algorithm for dangerous goods in airport security inspection, Asia-Pacific Conference on Communications Technology and Computer Science. (2021) 43-46.
 He Wen et al., Medical X-Ray image enhancement based on wavelet domain homomorphic filtering and CLAHE, International Conference on Robots & Intelligent System. (2016) 249-254.
 Wilson et al., Review of short-wave infrared spectroscopy and imaging methods for biological tissue characterization, Journal of Biomedical Optics. 20(3) (2015) 01-10.
 Pereira, Anjos and Marcel, Heterogeneous face recognition using domain-specific units, IEEE Transactions on Information Forensics and Security, 14(7), (2019) 1803-1816.
 Wang et al., Temporal segment networks: Towards good practices for deep action recognition, European Conference on Computer Vision, Springer. (2016) 20-36.
 Maya Harel et al., Tone mapping for shortwave infrared face images, IEEE 28th Convention of Electrical & Electronics Engineers in Israel. (2014) 1-5.
 Zhang, Yi, Lei and Li, Regularized transfer boosting for face detection across the spectrum, Signal Processing Letters IEEE. 19 (2012) 131-134.
 Shoja Ghiass, Arandjelovio, Bendada and Maldague, Infrared face recognition: A comprehensive review of methodologies and databases, Pattern Recognition, 47 (2014) 2807-2824.