Automated, Long-Range, Night/Day, Active-SWIR Face Recognition System

Brian E. Lemoff, Robert B. Martin, Mikhail Sluch, Kristopher M. Kafka, Andrew Dolby, Robert Ice WVHTC Foundation, 1000 Technology Drive, Suite 1000, Fairmont, WV, USA 26554

ABSTRACT

Covert, long-range, night/day identification of stationary human subjects using face recognition has been previously demonstrated using the active-SWIR Tactical Imager for Night/Day Extended-Range Surveillance (TINDERS) system. TINDERS uses an invisible, eye-safe, SWIR laser illuminator to produce high-quality facial imagery under conditions ranging from bright sunlight to total darkness. The recent addition of automation software to TINDERS has enabled the autonomous identification of moving subjects at distances greater than 100 m. Unlike typical cooperative, short range face recognition scenarios, where positive identification requires only a single face image, the SWIR wavelength, long distance, and uncontrolled conditions mean that positive identification requires fusing the face matching results from multiple captured images of a single subject. Automation software is required to initially detect a person, lock on and track the person as they move, and select video frames containing high-quality frontal face images for processing. Fusion algorithms are required to combine the matching results from multiple frames to produce a high-confidence match. These automation functions will be described, and results showing automated identification of moving subjects, night and day, at multiple distances will be presented. Keywords: Face Recognition, SWIR, Night Vision, Surveillance, Biometrics, Active Imaging

1. INTRODUCTION

The capability to covertly detect and identify people at long distances would be of great value to the military, law enforcement, and private security communities. Of most interest would be a capability that works night or day, under conditions ranging from bright sunlight to total darkness. Such a capability does not currently exist. While there are several biometric modalities commonly used to identify individuals, including DNA, fingerprint, iris, and face recognition, only face recognition has the potential to be of use at long distances. In an effort to develop this capability, the West Virginia High Technology Consortium Foundation (WVHTCF), under a research contract from the Office of Naval Research (ONR) and oversight from the Office of the Secretary of Defense Deployable Force Protection Program (DFP), is developing the Tactical Imager for Night/Day Extended Range Surveillance (TINDERS), an active short-wave infrared (SWIR) imaging system that illuminates targets with an invisible and eye-safe SWIR laser beam and matches SWIR facial images against mug-shots enrolled in a visible-spectrum database for identification.1,2,3 TINDERS nighttime face recognition results for stationary targets have been published at distances of 100 m, 200 m, and 350 m. A practical system, however, must be able to identify people in the distance as they move around naturally, since a covert identification system cannot expect subjects to stand still and look directly at the camera. Thus, a system must have an automated capability to detect people, track them as they move, capture good facial images from video, and process them for face recognition. These automated capabilities have recently been developed for the TINDERS system and are described in this paper.

1.1 Background

Detailed motivations for and descriptions of the TINDERS system were previously published,1,2,3 and are summarized in this section. Active-SWIR imaging has a number of advantages over other imaging modalities that make it uniquely suitable for long-range night/day human identification. Traditional visible-spectrum imagery produces the most recognizable facial images, but at night, there is not enough light to make a long-range close-up facial image. A powerful spotlight could be used to illuminate the face, but this would not be covert, and the required intensity would pose an eye-safety hazard. Thermal infrared imagery is commonly used for long-range nighttime surveillance, but the imagery produced does not correlate well to visible-spectrum mug shots. Active near infrared (NIR) imagery is an excellent modality for shorter-range nighttime face recognition, as the facial imagery correlates well to visible-spectrum mug-shots; however, the NIR illumination power required for close-up face images at distances beyond 100 m poses a

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Figure 2 includes both a conceptual illustration and a photograph of the TINDERS prototype hardware. The TINDERS system consists of three physical units: an optical head that sits on a pan-tilt (PT) stage; an electronics box that provides power, light (through and optical fiber), and communications to the optical head; and a computer that runs the user interface, low-level camera control functions, system automation, and face recognition software. The TINDERS optical head includes both the SWIR illuminator optics and the imager. In the current version of the hardware, the optical head weighs roughly 30 pounds and sits in an environmentally-controlled enclosure atop a commercial pan-tilt stage. The imager and illuminator pan, tilt, and zoom together so that the illuminator beam is always just filling the imager field of view. This serves to maximize the image signal level and avoid wasted light. The illuminator light source, located in the electronics box, delivers a maximum power of 5W to the optical head through an optical fiber in the umbilical. Because the illumination beam is expanded to 5-inches in diameter prior to exiting the optical enclosure, the TINDERS illuminator is safe to the unaided eye at point-blank range.

1.2 Automation Strategy

As discussed above, even with high-quality, short-range SWIR facial imagery from a stationary, frontal face, the single-image success rate is on the order of 70%. Thus, high-confidence identification of noncooperative, moving targets, at long distances will require the fusion of face matching results from multiple SWIR facial images known to be of the same person. Ideally this process would be fully-automated, to allow for unattended operation. The individual automation processes required for a fully automated system include:

Detection of an individual and designation of that individual to be tracked;

Tracking of the individual as they move;

Zooming in on the face of the moving target;

Capturing video frames containing frontal facial images of sufficient quality for identification;

Submission of captured facial images to face recognition software;

Fusion of the matching results from multiple video frames;

Thresholding to determine whether fused matching result has enough confidence to report as a match;

Reporting the positive identification result. As previously reported2, a cascade pattern matching algorithm was developed to detect upper bodies. This algorithm is capable of automatically detecting people at distances up to 3 km as long as the field of view is wide enough to include the full upper body. To implement full automation, a rule would need to be applied to determine when a detected person should or should not be tracked. In lieu of this, TINDERS displays a box around all detected people, and an operator can click on the box in order to designate the person to be tracked. For tracking, TINDERS currently uses an SLA-2000 video processing board5, a commercial product primarily used for tracking ground objects in aerial surveillance video. The TINDERS video is processed by this board, and when a detected person is designated for tracking, the tracking box coordinates are sent to the board, which updates the target position after each frame. The updated tracking coordinates are then used to calculate a velocity vector that is sent to the pan-tilt stage to keep the tracked target as close to the center of the imager field of view as possible.

Once a person is being tracked with a wide field of view, a fully-automated solution would automatically zoom in on the target while continuing to track. This has not yet been implemented in TINDERS, so zoom is still controlled by an operator. At narrower fields of view, another cascade algorithm, also previously reported2, detects faces. As with the upper-body detection algorithm, a box is displayed around the detected face, and an operator can click on the box to initiate tracking on the face. Once the face box coordinates have been sent to the SLA-2000 for tracking, the head can still be tracked even when the person turns so that the face is no longer visible.

Once the field of view and distance are small enough for face recognition to be possible, TINDERS begins to evaluate detected faces for face recognition suitability. It was previously reported2 that eye-detection and nose-detection algorithms have been developed to determine whether a frontal face with clear features is present in the video frame. At 30 frames per second, the algorithms typically run fast enough to search one third of the frames for good faces. When a “good” face is detected, it is placed on a queue of images to be processed for face recognition. As new “good” faces are detected, they are placed at the front of the queue so that the face recognition software will always be processing the most recently detected “good” face image. The face recognition software processes SWIR face probe images from the queue one at a time, matching them against a visible-spectrum database of facial images. After each probe image is matched, the results are fused with the previous results, and the fused results are displayed.

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This researchoversight froacknowledgeUSA, and the

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REF

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DISCUSSION

NDERS active-bilities that woomated capabilitracking were

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the face of an functions. Sible.

OWLEDGM

09-C-0064 fromience and Tecason Stanley, Wification Techn

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