Weekly Tech Roundup | Mar 15, 2019

Weekly tech roundup: facial recognition technology edition

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This week’s tech roundup includes just a few of the latest facial recognition technologies available at the moment. All of them are opportunities for entrepreneurs and businesses to license and bring to the commercial or military market.

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Here is this week’s facial recognition technology roundup:

cFaces facial recognition software

Navy researchers have invented powerful facial recognition software. They developed an improved approach specifically for watchlist surveillance that replicates human facial recognition processes. The prototype system, known as cFaces, is capable of autonomously recognizing a person of interest and does not need to search any image database, nor does it need to match or rank images as a precursor to recognition.

Unlike other facial recognition systems, cFaces preserves individual privacy, storing only the images of known bad actors on the watchlist, and by limiting access to authorized users.

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Long wavelength infrared imaging for advanced facial recognition

Long wavelength infrared (LWIR) imaging is generally a passive tool because it measures a signal emitted by an object and does not require an interrogating light source. One disadvantage of LWIR imaging is the lack of contrast relative to background objects and noise. Images of people tend to look washed out and ghostlike. It is difficult to obtain information from the images, which would allow reliable and repeatable identification of a person's unique facial or other features.

Army scientists and engineers have greatly improved upon LWIR to enhance the identifiability of a person. This device and system generate a 3-D model of the surface of a face or any object emitting thermal radiation using an image captured by a thermal camera.

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2-D facial recognition from thermal images or images taken from different angles

Army scientists have developed a method and system for facial recognition when the face image in the database and the acquired image are in different modalities or have different pose angles, usually due to the fact that they are acquired by different sensors or at different times.

The system converts the coordinates of the eyes and mouth of a facial image appearing in a randomly oriented photograph to virtual coordinates corresponding to an estimate of how the head and facial coordinates would appear if the subject's head were turned such that the centers of the eyes and corner of the mouth were oriented in a vertical plane.

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Adaptive facial recognition software

Navy scientists have developed a new facial recognition software algorithm that uses biometric functionality and requires very little training data to be effective. The new algorithm can tolerate changes in viewpoint as well as misalignments and changes in illumination and scale. It is particularly effective where data from multiple sensors is available. Access to data can also be customized and limited. The developed methodology focused on facial recognition; however, the techniques are expandable to other domains such as ship and target recognition.

Current military applications include electronic warfare specific emitter identification. Commercial applications include airport and public facility security and surveillance.

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Facial recognition via thermal imaging

To date, face recognition research has predominantly focused on the visible spectrum, addressing challenges such as illumination variations, pose, and image resolution. However, for surveillance during nighttime, the lack of illumination prevents cameras operating in the visible light spectrum from being used discreetly and effectively.

As most databases and watch lists only contain facial imagery in the visible spectrum, it is difficult to match an unknown thermal image to a set of known visible images. This is referred to as cross-modal face recognition. The Army’s solution is a cross-modal face matching system using polarimetric thermal image data. Polarimetric imaging in the thermal spectrum is sensitive to changes in surface texture and geometry.

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Headshot Image of Austin Leach, PhD, CLP

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