| NSF Org: |
CNS Division Of Computer and Network Systems |
| Recipient: |
|
| Initial Amendment Date: | March 20, 2019 |
| Latest Amendment Date: | March 20, 2019 |
| Award Number: | 1923184 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Jeremy Epstein
jepstein@nsf.gov (703)292-8338 CNS Division Of Computer and Network Systems CSE Direct For Computer & Info Scie & Enginr |
| Start Date: | October 1, 2018 |
| End Date: | September 30, 2022 (Estimated) |
| Total Intended Award Amount: | $300,002.00 |
| Total Awarded Amount to Date: | $300,002.00 |
| Funds Obligated to Date: |
|
| History of Investigator: |
|
| Recipient Sponsored Research Office: |
3400 N CHARLES ST BALTIMORE MD US 21218-2608 (443)997-1898 |
| Sponsor Congressional District: |
|
| Primary Place of Performance: |
1101 E 33rd St Baltimore MD US 21218-3637 |
| Primary Place of Performance Congressional District: |
|
| Unique Entity Identifier (UEI): |
|
| Parent UEI: |
|
| NSF Program(s): | Secure &Trustworthy Cyberspace |
| Primary Program Source: |
|
| Program Reference Code(s): |
|
| Program Element Code(s): |
|
| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.070 |
ABSTRACT
Many physical characteristics, such as face, fingerprints, and iris as well as behavioral characteristics such as voice, gait, and keystroke dynamics, are believed to be unique to an individual. Hence, biometric analysis offers a reliable solution to the problem of identity verification. It is now widely acknowledged that biometric systems are vulnerable to manipulation where the true biometric is falsified using various attack strategies; such attacks are referred to as Presentation Attacks (PAs). This project develops computational imaging-based methods for detecting known and unknown PAs for face and iris biometric systems.
While biometrics has been an active field of research for more than three decades, the design of biometric systems that are robust to a diverse set of known and unknown PAs is still in its infancy. This project seeks to build biometric systems that integrate computational imaging sensors for acquiring physiological, spectral, and material properties with the goal of detecting and mitigating the effects of known and unknown PA attacks. Specifically, as part of the project novel computational cameras will be built that augment traditional sensors used for face and iris recognition by adding subsystems that measure changes in a subject's physiology including vital signs, blood perfusion, voluntary and involuntary responses to external stimuli as well as composition as measured in scattering, reflectance and spectral profiles. The rich signatures sensed by these computational cameras is expected to provide a robust characterization of the true biometric and its variations while providing high discriminability to commonly used PAs. Further, by virtue of outlier detection and open-set modeling, this project develops a highly flexible approach for detecting previously unseen PAs.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
Note:
When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).
Some links on this page may take you to non-federal websites. Their policies may differ from this site.
PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
The vast majority of the literature in presentation attack detection (PAD) is based on liveness detection techniques (i.e. systems which aim to detect signs of life) using off-the-shelf (OTS) video cameras.
Since it is inherently more difficult to spoof multiple modalities and systems simultaneously, multimodal biometrics systems have also been proposed in the literature for PADs. Even in these systems PAs remain a serious cause of concern, as many biometric systems remain vulnerable to the simplest forms of spoofing attack. Existing approaches remain fundamentally limited to liveness detection, with some limited work in the use of multispectral cameras for material identification.
Our main goal of this project was to build end-to-end biometric systems that integrate computational imaging sensors for acquiring physiological, spectral and material properties which can be used to detect and mitigate the effects of known and unknown PAs. To this end, we developed programmable hyperspectral sensors that can optically detect differences in materials. In addition, we developed novel anomaly detection and federated learning-based methods for detecting novel known and unknown PAs. Eight conference papers and seven journal papers have been published which describe our findings in this project. Implementation code corresponding to many methods developed in this project have been made publicly available.
Last Modified: 01/31/2023
Modified by: Vishal M Patel
Please report errors in award information by writing to: awardsearch@nsf.gov.
