US Department of Homeland Security

AI Use Case Inventory

2025 Inventory

Source data: DHS
Glossary
DHS-407

Biometric Check-in for ATD-ISAP (SmartLINK)

ICE
Deployed Presumed high-impact but determined not high-impact Computer Vision Law Enforcement

Problem Statement

This use case intends to solve the problem of the need for frequent in-person check-ins for participants in the ATD-ISAP program.

Expected Benefits

ISAP Biometric Monitoring App is a technology option that allows participants to report in using a smartphone. This app verifies a participant’s identity, determine their location, and quickly collect status change information. The app adds functionality not available with telephonic and is less intrusive than a GPS unit. ISAP monitoring app limits in-person interactions of routine check-ins, allowing more time to be allocated to non-compliant participants, complex removal proceedings cases and docket management.

System Outputs

There are two outputs related to using ISAP Biometric Monitoring App. Either a participant “passes” (biometric match) or the photo is moved to a “pending review” status. In either scenario, a human can evaluate the response.

Impact Justification

Facial verification is only one option for check-in. If the remote check-in fails, either because it was unable to verify the match between the user and their previously taken photo, or because of other potential issues (poor lighting, camera/phone malfunction, etc...), an officer will manually review the check-in photo against the previously taken photos. If that fails, the user can schedule an in-person check-in at their local ERO office. Therefore, the output of AI (facial verification for a remote check-in) is not the primary basis for a decision or action that would affect the individual's rights or safety. It is a convenience to help save time for both the user and officers.

Documentation

Operational Date: 2/1/18
Procurement: a) Purchased from a vendor
Vendor(s): BI
ATO: Yes
System Name: ISAP IV (SmartLink) IATSS

Data & Code

Training Data: The training process includes datasets from diverse facial images and real-world environments. The images are preprocessed to normalize variables like lighting and facial expressions, making them suitable for facial matching. Data techniques, such as rotation and scaling, are also applied to alleviate the need for additional data collection. The models are trained to extract facial features and to match them accurately.
PII Involved: Yes
Demographic Variables: None
Custom Code: No