Low Probability of False Alarm (Low-Pfa) Algorithm for on-person screening.
Description
Increase passenger throughput via improve detection performance and decreasing alarm rates and passengers touch rates by 50%
Detailed example
The AI outputs target coordinates to the operator viewing station which is viewed as a bounding box on a representative human figure.
AI / analytics pattern
Computer Vision: AI that processes and interprets visual data (e.g., images and videos).
Automation level / stage
c) Deployed – The use case is being actively authorized or utilized to support the functions or mission of an agency.
Expected benefit
The purpose is to reduce alarm rates while providing increased passenger throughput and experience. Utilizes Machine Learning (ML) to improve detection performance while decreasing alarm rates and passengers touch rates. The algorithm is gender agnostic which no longer requires officers to select a passengers gender prior to being scanned. Advanced imaging technology (AIT) throughput and AIT utilization have increased with this new algorithm. Note: Once the algorithm is trained, it is locked down and no longer learning.
Controls / human review
ATO: No; PIA: https://www.dhs.gov/sites/default/files/publications/privacy-tsa-pia-32-d-ait.pdf
Data needed
Vendor AITs are tested in a laboratory environment using mock passengers. Statistical tests are done for false and true alarms, a performance measure for detection. Statistical tests are done for probability of false alarm and probability of detection, a performance measure for detection capability.