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System for Nighttime and Low-Light Face Recognition

Award Information
Agency: Department of Defense
Branch: Special Operations Command
Contract: H9240518P0001
Agency Tracking Number: S18A-001-0006
Amount: $149,911.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: SOCOM18A-001
Solicitation Number: 2018.0
Timeline
Solicitation Year: 2018
Award Year: 2018
Award Start Date (Proposal Award Date): 2018-06-14
Award End Date (Contract End Date): 2018-12-14
Small Business Information
600 West Cummings Park
Woburn, MA 01801
United States
DUNS: 964928464
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Jeffrey Byrne
 (781) 503-3299
 jeffrey.bryne@stresearch.com
Business Contact
 Joseph Larocque
Phone: (339) 999-2242
Email: joseph.larocque@stresearch.com
Research Institution
 Northeastern University
 Jamie Hackney
 
360 Huntington Ave
Boston, MA 02115
United States

 (617) 373-3266
 Nonprofit College or University
Abstract

Face recognition performance using deep learning has seen dramatic improvements in recent years. This improvement has been fueled in part by the curation of large labeled training datasets with millions of images of hundreds of thousands of subjects.This results in effective generalization for matching over pose, illumination, expression and age variation, however these datasets have traditionally only focused on visible spectrum imageryCovert face recognition for the warfigher requires matching non-visible spectrum imagery against watch-lists derived from current visible facial databases.However, it would be impractical to collect non-visible training datasets at the scale necessary for end-to-end training.In this proposal, STR, and university partner Northeastern University, will investigate cross-modal transfer learning by applying domain transfer techniques for matching a visible face representation to low-light and night-time sensor data.The STR team will collect a cross-spectrum biometric dataset from LWIR/MWIR/NIR sensors in support of a feasibility study on this cross-modal transfer learning capability, leveraging the STR Janus face matcher for baseline performance evaluation.

* Information listed above is at the time of submission. *

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