Last updated July 19, 2019 at 1:38 pm
Scientists have enhanced a facial recognition algorithm that improves the odds of identifying a person in difficult environments.
Scientists from DST have improved facial recognition technology, which will enhance the odds of identifying someone in adverse environments such as across a distant carpark and in dark alleys.
Sau Yee Yiu and Dmitri Kamenetsky are both members of DST’s biometrics research team who worked on the new algorithm.
“Biometrics is all about recognising people,” Kamenetsky explains. “We are asking is that the same person as this one in our dataset?”
“While iris and fingerprint biometric data are the most accurate, a comparison of facial characteristics is the most common technique used because it’s reasonably accurate and CCTV footage is commonplace.”
New algorithm can identify people in difficult environments
The aim of the research was to gauge if facial recognition algorithms could be used in adverse environments, for example at long distances up to 250 metres and in dark environments such as an alley or a moonless night.
Enhancements were made to an in-house facial recognition algorithm. The team then trialled the new software with long lenses across fields on bright sunny days.
However, the team wanted to test the technology further. They set the facial recognition to the test using a dark tunnel facility, where the scientists could barely see their hands when the lights were turned off.
The software passed the test with flying colours. They found that with the new algorithm, the face recognition does in fact work in difficult enviornments.
Facial recognition overcomes atmospheric turbulence
Yiu says it was a literature review in the early stages that helped direct the team’s energy. After this, they were able to come up with a model for the new software.
“I then came up with a model of how heat propagates through the atmosphere, and this turns out to be similar to the way noise from atmospheric turbulence distorts images over long distances. The atmosphere moves and shifts around and your image gets sheared and blurry. Applying my heat dispersal model gets rid of that turbulence and brings it back closer to a focused, sharp image,” says Yiu.
The low-light enhancement uses various filter passes to remove graininess from images.
Another benefit of the new algorithm is that it can be tweaked interactively by using an interface that allows several parameters to be controlled by sliders.
This controls the deconvolutions applied to the images.
As the user moves the sliders the output will be updated in real-time, allowing a tailoring of the algorithm to get the best results for a particular environment.
The team presented its results at the 2018 Digital Image Computing: Techniques and Applications (DICTA) conference. In the paper the colleagues demonstrate the improvements in recognition and face matching delivered by the enhanced algorithm. A further algorithm was used to calculate a metric for the overall quality of facial images. This revealed that images processed with the modified algorithm all had superior quality to the originals, concurring with visual checks.
“We’re very happy with the results, which will be of benefit to stand-off surveillance systems,” says Kamenetsky.
“We’ve released a description of the algorithm, allowing other researchers to implement it and make further improvements. Interestingly, most of the research presented at DICTA was using deep learning in some way, ours is just a relatively simple yet effective mathematical approach.”
Image Enhancement for Face Recognition in Adverse Environments, Dmitri Kamenetsky, Sau Yee Yiu, Martyn Hole, DICTA 2018.