Army researchers have developed an artificial intelligence and machine learning technique that produces a visible face image from a thermal image of a person’s face captured in low-light or nighttime conditions. This development could lead to enhanced real-time biometrics and post-mission forensic analysis for covert nighttime operations.
Thermal cameras like FLIR, or Forward Looking Infrared, sensors are actively deployed on aerial and ground vehicles, in watch towers and at check points for surveillance purposes. More recently, thermal cameras are becoming available for use as body-worn cameras. The ability to perform automatic face recognition at nighttime using such thermal cameras is beneficial for informing a Soldier that an individual is someone of interest, like someone who may be on a watch list.
The motivations for this technology — developed by Drs. Benjamin S. Riggan, Nathaniel J. Short and Shuowen “Sean” Hu, from the U.S. Army Research Laboratory — are to enhance both automatic and human-matching capabilities.
“This technology enables matching between thermal face images and existing biometric face databases/watch lists that only contain visible face imagery,” said Riggan, a research scientist. “The technology provides a way for humans to visually compare visible and thermal facial imagery through thermal-to-visible face synthesis.”
He said under nighttime and low-light conditions, there is insufficient light for a conventional camera to capture facial imagery for recognition without active illumination such as a flash or spotlight, which would give away the position of such surveillance cameras; however, thermal cameras that capture the heat signature naturally emanating from living skin tissue are ideal for such conditions.
“When using thermal cameras to capture facial imagery, the main challenge is that the captured thermal image must be matched against a watch list or gallery that only contains conventional visible imagery from known persons of interest,” Riggan said. “Therefore, the problem becomes what is referred to as cross-spectrum, or heterogeneous, face recognition. In this case, facial probe imagery acquired in one modality is matched against a gallery database acquired using a different imaging modality.”
This approach leverages advanced domain adaptation techniques based on deep neural networks. The fundamental approach is composed of two key parts: a non-linear regression model that maps a given thermal image into a corresponding visible latent representation and an optimization problem that projects the latent projection back into the image space.
Details of this work were presented in March in a technical paper “Thermal to Visible Synthesis of Face Images using Multiple Regions” at the IEEE Winter Conference on Applications of Computer Vision, or WACV, in Lake Tahoe, Nevada, which is a technical conference comprised of scholars and scientists from academia, industry and government.
At the conference, Army researchers demonstrated that combining global information, such as the features from the across the entire face, and local information, such as features from discriminative fiducial regions, for example, eyes, nose and mouth, enhanced the discriminability of the synthesized imagery. They showed how the thermal-to-visible mapped representations from both global and local regions in the thermal face signature could be used in conjunction to synthesize a refined visible face image.
The optimization problem for synthesizing an image attempts to jointly preserve the shape of the entire face and appearance of the local fiducial details. Using the synthesized thermal-to-visible imagery and existing visible gallery imagery, they performed face verification experiments using a common open source deep neural network architecture for face recognition. The architecture used is explicitly designed for visible-based face recognition. The most surprising result is that their approach achieved better verification performance than a generative adversarial network-based approach, which previously showed photo-realistic properties.
Riggan attributes this result to the fact the game theoretic objective for GANs immediately seeks to generate imagery that is sufficiently similar in dynamic range and photo-like appearance to the training imagery, while sometimes neglecting to preserve identifying characteristics, he said. The approach developed by ARL preserves identity information to enhance discriminability, for example, increased recognition accuracy for both automatic face recognition algorithms and human adjudication.
As part of the paper presentation, ARL researchers showcased a near real-time demonstration of this technology. The proof of concept demonstration included the use of a FLIR Boson 320 thermal camera and a laptop running the algorithm in near real-time. This demonstration showed the audience that a captured thermal image of a person can be used to produce a synthesized visible image in situ. This work received a best paper award in the faces/biometrics session of the conference, out of more than 70 papers presented.
Riggan said he and his colleagues will continue to extend this research under the sponsorship of the Defense Forensics and Biometrics Agency to develop a robust nighttime face recognition capability for the Soldier.
The Latest on: Real-time biometrics
via Google News
The Latest on: Real-time biometrics
- Prysmian Group launches Pry-ID, a new smart solution acting as a digital fingerprint for cableson September 7, 2019 at 5:02 am
Prysmian Group, world leader in the energy and telecom cable systems industry, is launching its Pry-ID smart cable technology, which provides easy, real-time access to comprehensive ... Pry-ID acts as ...
- Iris ID announces IrisAccess biometric platform integration with LEAF access cardson September 7, 2019 at 4:35 am
For real time data, safeguards against repeated false matches are ... The principles are no different from obtaining a fingerprint to confirm identity, where consent would normally be given. For ...
- Finger vein recognition and government programs top this week’s biometrics and digital ID newson September 6, 2019 at 6:46 am
Less common biometrics like finger vein and hand geometry recognition ... to detect future mass killers with “a multi-modality solution, along with real-time data analytics,” according to a Gizmodo ...
- Featurespace behavioral biometrics to secure HSBC against money launderingon September 5, 2019 at 1:01 pm
The Adaptive Behavioral Biometrics technology to detect and prevent fraud during customer onboarding and digital sessions was introduced to the ARIC Platform in June. Featurespace’s ARIC Platform uses ...
- Lufthansa Expands Biometric Boarding Technology to New York JFK’s Terminal 1on September 5, 2019 at 9:59 am
Biometric boarding relies on data provided by the CBP, a central source of information that allows for exceptional efficiency while still prioritizing passengers’ safety and security. The real-time ...
- Biometric coupons will help you get seats in gen compartmentson September 4, 2019 at 6:05 pm
An RPF cop will be positioned near the unreserved coaches of the platforms with a small biometric machine ... the country keep track of passenger as well as goods trains on real-time basis,” he said.
- UK court backs police use of face recognition, but fight isn't overon September 4, 2019 at 10:43 am
Bridges may have been snapped during a pilot called AFR Locate. This involved taking pictures of people’s faces from live CCTV feeds and processing them in real time to extract biometric information.
- Bitcoin IRA™ Now Offers Advanced Biometric Security For Its 24/7 Self-Trading IRA Platformon September 4, 2019 at 2:00 am
Advanced Biometric Security feature is available ... execute cryptocurrency trades in real-time 24/7 through a leading OTC liquidity partner and then move the funds into an industry-leading ...
- Biometrics Middleware Market Investigation and Growth Forecasted Until the End of 2027on September 3, 2019 at 7:50 pm
Biometrics middleware will witness high demand with growing reliance of enterprises on the biometric technologies for security. Impact of Real-Time Data Processing on End-to-End Biometric Services ...
via Bing News