Human Identification From a Distance

1. Human Identification From a Distance D. Adjeroh, B. Cukic, M. Gautam, L. Hornak, A. Ross Lane Department of CSEE West Virginia University NC-BSI, December 2008

2. Problem Statement • Surveillance datasets acquired at border zones offer an opportunity to recognize individuals from a distance rather than requiring close visual inspection. The project will develop methods for human identification from surveillance videos. • Methodology • Develop a hierarchical approach to human recognition from a distance. • Define event clustering in joint biometric – surveillance space. • Search methods: from events to biometric profiles and vice versa. NC – BSI 2008

3. (66× , 50m) (109×, 100m) (153×, 150m) (284×, 300m) Note: (magnification, distance) approximately the same resolution: 60 pixels between the eyes. Image Quality at a Distance High Sensitivity to • Motion blur: because of long focal distance • Out-of-focus Blur: because of small DOF • Distortion due to lens • Low pixel count: (sensor resolution is limited) • Magnification blur (due to high magnification) NC – BSI 2008

4. 60 pixels 85 pixels 35 pixels Effect of Frame Resolution Rank 1 10x, 52f 10x, 31f 15x, 31f CMC curves NC – BSI 2008

5. 100% roof light 50% roof light No roof light Effect of Illumination CMCs Degradations in high magnification images: • Sensor noise • Magnification blur • Motion blur • Out of focus blur • Zoom blur • Atmospheric blur • Illumination • Contrast • Resolution Rank 1 Probes: 20x magnification 52 feet, 50 pixels inter-eye distance NC – BSI 2008

6. Soft biometric traits Jain et al, “Utilizing soft biometric traits for person authentication”, Proc. International Conference on Biometric Authentication (ICBA), Hong Kong, July 2004 NC – BSI 2008

7. Human Metrology 2D measurementssuperimposed on 3Dimages (3D images from Allen et al, 2004) • 2D Model • Available from video • Possible multiple views in surveillance • MAT Representation • Medial Axis Transform • Less detailed, but may be adequate for required representation Decorated MAT Representation (for 2D) MAT Representation (1D) Multiresolution MATs NC – BSI 2008

8. Extending the Application Envelope: Virtual Identities in Space/Time • Correlate Two Surveillance Videos Between Aldgate East and Liverpool Street tube stations Between Russell Square and King's Cross tube stations At Edgware Road tube station On bus at Tavistock Square http://news.bbc.co.uk/1/hi/uk/4661059.stm http://en.wikipedia.org/wiki/Image:Londonbombing2.jpg NC – BSI 2008

9. Extending the Application Envelope (2) NC – BSI 2008

10. Biometric Surveillance Space NC – BSI 2008

11. Decomposing a Video Stream NC – BSI 2008

12. Retrieval/Analysis Paradigms NC – BSI 2008

13. Leverage • The Center for Identification Technology Research (NSF I/UCRC). • Biometrics: Performance, Security and Social Impact, (NSF and DHS – Human Factors) • Biometric recognition from video streams, data collection. • Night time biometrics (ONR). • Video/image compression. NC – BSI 2008

14. Deliverables Years 2-6: • Architecture of the joint identity-surveillance space, • Efficient segmentation and labeling algorithms, • Fusion algorithms for identification from surveillance video, • Storage and retrieval architecture. • System evaluation. NC – BSI 2008