Agenda

1. Usability Considerations for Face Image Capture at U.S. Ports of EntryNIST International Workshop on Usability and Biometrics June 23-24, 2008Lawrence D. Nadel, Ph.DNoblis

2. Agenda • US-VISIT background • Potential face recognition applications in US-VISIT • Port of entry (POE) operational environments • Usability considerations

3. US-VISIT Background • US‑VISIT provides biometric identification and analysis services to agencies throughout the immigration and border management, law enforcement and intelligence communities • US‑VISIT’s services help decision makers accurately identify people and assess whether they pose a risk to the United States • Biometrics captured from non-US citizens, ages 14-79 • Biometrics captured at Entry – enhances security and facilitates legitimate travel • Fingerprints • 10P enrollment/background check, four‑finger verification • Exit verification (under development) • Facial image • Human verifiable traveler history • Currently no automated face recognition • Biometrics captured at Exit (to be determined) – identify overstays, crosscheck watch lists

4. Applications for Face Imaging/Recognition • Human comparison • Compare live photo with visa and/or past photos for visitors who have not been fingerprinted • Compare live photo with e-Passport photo for first time Visa Waiver Program visitors • Increase verification confidence through decision level or score level fusion • Supplement fingerprint check for detecting aliased (duplicate) records or fraud • Search face-only watch lists

5. Diversity of Operational Environments,Use Cases, and Travelers • POE types • Land – pedestrian, car, truck, bus • Air – small plane, jumbo jet • Sea – small boat, cruise ship • Ambient environment - indoor/outdoor • Variable illumination - day/night, directional, multi-spectral • Entry – formal inspection stations • Exit – little or no inspection infrastructure • Travelers • Cooperative, non-cooperative, uncooperative • Multiple languages, cultures, appearances/clothing • Hands full - luggage, packages, small children

6. Air POE Environment • Key factors for face recognition • Pose angle • Interocular pixel resolution • Illumination • Subject distance from camera (head size, distortion) • Background

7. Pedestrian Exit Land POE Examples Entry

8. Usability-Related Interactions Workstation/ System Traveler CBP Officer

9. Cooperative Traveler • Indicate that a picture is being taken—where and when • Image capture sensor should look and sound like a camera • Provide simple and clear guidance (oral/written, foreign language, still images, video) • Limit physical degrees of freedom, e.g., indicate where feet should be placed on floor to control distance to camera • Accommodate traveler whose hands may be occupied, e.g., baggage, small child • Align camera with user’s face—accommodate variable height (short/tall, standing, wheelchair); multiple cameras, portrait mode, wide field of view digital camera

10. Non-cooperative Traveler* • Human factors engineering to direct traveler’s gaze at camera and have traveler pause for photo – no conscious effort on part of traveler required • Printed signage • Video display (static or variable) • Strategic chokepoint • Top of escalator • Turnstile • Portal * For US-VISIT Exit

11. (Illustration courtesy of NIST Usability Group) CBP Officer • Officer needs to review documents, operate workstation, and interview and observe traveler. Position equipment to minimize officer movement and minimize use of peripheral vision. • Simple and logical workstation GUI. • For officer positioning of camera, show geometric overlay on video screen to indicate proper placement and size of image to be captured. Provide easy to use automated or manual camera control.

12. Workstation/System • Audio-visual feedback to both traveler and officer • Automated image capture (“quality in the loop”) • Automated control of camera focus, exposure • Electronic control of pan-tilt-zoom (PTZ) camera • Automated face finding and quality assessment algorithms • Select image • Crop

13. Quality-in-the-Loop – Face Image Quality Improvement and Face Recognition Study • Select and assess representative cameras • Webcams • Video with Pan-Tilt-Zoom • Digital Still • Wide Dynamic Range • Select and assess several face quality metric software tools • Inter-eye distance, head position, face contrast, lighting uniformity, … • Integrate selected cameras; capture photos and video streams; run image quality software post-capture to assess impact • Determine “best” hardware/software combination; integrate to run real time; assess potential impact on image selection

14. Simulated Demonstration of “Quality in the Loop” for Image Selection (Webcam) (0-10) (sec.)

15. Discussion Lawrence D. Nadel, Ph.D. Phone: (703) 610-1677 Email: nadel@noblis.org