You are here
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
DUNS:
964928464
HUBZone Owned:
N
Woman Owned:
N
Socially and Economically Disadvantaged:
N
Principal Investigator
Name: Jeffrey Byrne
Phone: (781) 503-3299
Email: jeffrey.bryne@stresearch.com
Phone: (781) 503-3299
Email: jeffrey.bryne@stresearch.com
Business Contact
Name: Joseph Larocque
Phone: (339) 999-2242
Email: joseph.larocque@stresearch.com
Phone: (339) 999-2242
Email: joseph.larocque@stresearch.com
Research Institution
Name: Northeastern University
Contact: Jamie Hackney
Address: 360 Huntington Ave
Boston, MA, 02115
Phone: (617) 373-3266
Type: Nonprofit college or university
Contact: Jamie Hackney
Address: 360 Huntington Ave
Boston, MA, 02115
Phone: (617) 373-3266
Type: 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. *