E.1.2
A Possible Technological Approach to Addressing the Threat

Checkpoint screening of airport passengers and their baggage to prevent the transport of weapons (e.g., firearms, explosives) will continue. However, with advancing technologies, future security checkpoints could be different from today’s checkpoints in several ingenious respects:

  • Use of new sensors. New imaging sensors could be introduced to reveal whether weapons are being hidden under clothing, although these sensors might also reveal anatomical features of the body. Retinal scans and other biometrics could be introduced to help validate passenger identity. Sensors for thermal imaging of the body or portions of the body could be introduced to detect signs of nervousness or excitement, and additional video cameras could be introduced with new software for face recognition and for analyzing body motion to search for signs of nervousness and other suspicious activity. Some of these sensors could be positioned so that passengers are aware they are being sensed, while others might be positioned so that passengers have no specific, explicit warning that they are being sensed.

  • Use of real-time networking to share data instantaneously across multiple airport security checkpoints (both within the same airport and at different airports), and to integrate data with information in other databases. This approach would enable real-time sharing and fusion of information such as the detection that a nonstandard homemade briefcase containing unacceptable materials was found in airport A, and another similar event occurred in airport B, resulting in immediate transmission of information about the briefcase that would enable detecting other copies of it at other airports.

  • Use of data mining methods to draw inferences from a large shared data set, and to provide guidance to the human checkpoint operators. For example, computer-based screening profiles for luggage and passengers might be improved continuously based on experience with millions of passengers across many airports. As one example, consider that today a human operator decides to hand inspect a certain fraction of luggage after it has passed through the x-ray scanner, perhaps because a suspicious-looking object is seen in the x-ray scan. Each time this occurs, the result of the hand inspection could be provided as a training example to a data mining program so that it could learn, from hundreds of thousands of such experiences, which x-ray images correspond to truly dangerous objects as opposed to false alarms. Computer-based machine learning algorithms could use such training data, collected from many security checkpoints at many airports, to formulate a potentially more accurate profile that could automatically estimate a risk level for each object seen in an x-ray scan and to assist the human screener with the goal of reducing the number of false alarms leading to invasive manual searches.



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