A research team led by Dr. Alice O’Toole, a professor in The University of Texas at Dallas’ School of Behavioral and Brain Sciences, is evaluating how well these rapidly evolving recognition programs work. The researchers are comparing the rates of success for the software to the rates for non-technological, but presumably “expert” human evaluation.
“The government is interested in spotting people who might pose a danger,” O’Toole said. “But they also don’t want to have too many false alarms and detain people who are not real risks.”
Dr. Alice O’Toole is leading a team that is examining where facial-recognition algorithms succeed and where they come up short.
The studies in the Face Perception and Research Laboratories are funded by the U.S. Department of Defense. The agency is seeking the most accurate and cost-effective way to recognize individuals who might pose a security risk to the nation.
Algorithms – formulae that allow computers to “recognize” faces - vary greatly among the various software developers, and most have not faced real-world challenges. So O’Toole and her team are carefully examining where the algorithms succeed and where they come up short. They’re using point-by-point comparisons to examine similarities in millions of faces captured within a database, and then comparing results to algorithm determinations.
In the studies, humans and algorithms decided whether pairs of face images, taken under different illumination conditions, were pictures of the same person or different people.
The UT Dallas researchers have worked with algorithms that match up still photos and are now moving into comparisons involving more challenging images, such as faces caught on video or photographs taken under poor lighting conditions.
“Many of the images that security people have to work with are not high-quality,” O’Toole said. “They may be taken off closed-circuit television or other low-resolution equipment.”
The study is likely to continue through several more phases, as more and better software programs are presented for review. So far, the results of man vs. machine have been a bit surprising, O’Toole said.
“In fact, the very best algorithms performed better than humans at identifying faces,” she said. “Because most security applications rely primarily on human comparisons up until now, the results are encouraging about the prospect of using face recognition software in important environments.”
The real success comes when the software is combined with human evaluation techniques, O’Toole said. By using the software to spot potential high-risk individuals and then combining the software with the judgment of a person, nearly 100 percent of matching faces were identified, O’Toole said.
The researchers also are interested in the role race plays in humans’ ability to spot similar facial features. O’Toole said many studies indicate individuals almost always recognize similarities among members of their own race with more accuracy. But there is little research evaluating how technological tools differ in recognizing faces of varying races.
In a paper to be published soon in ACM Transactions on Applied Perception, O’Toole reports that the “other race effect” occurs for algorithms tested in a recent international competition for state-of-the-art face recognition algorithms. The study involved a Western algorithm made by fusing eight algorithms from Western countries and an East Asian algorithm made by fusing five algorithms from East Asian countries. At the low false-accept rates required for most security applications, the Western algorithm recognized Caucasian faces more accurately than East Asian faces, and the East Asian algorithm recognized East Asian faces more accurately than Caucasian faces.
Next, using a test that spanned all false-alarm rates, O’Toole’s team compared the algorithms with humans of Caucasian and East Asian descent matching face identity in an identical stimulus set. In this case, both algorithms performed better on the Caucasian faces, the “majority" race in the database. The Caucasian face advantage was far larger for the Western algorithm than for the East Asian algorithm.
Humans showed the standard other-race effect for these faces, but showed more stable performance than the algorithms over changes in the race of the test faces. These findings indicate that state-of-the-art face-recognition algorithms, like humans, struggle with “other-race face” recognition, O’Toole said.
The companies that develop the most reliable facial recognition software are likely to reap big profits down the line. Although governments may be their most obvious clients, there is also a great deal of interest from other major industries.
“Casinos have been some of the first users of face recognition software,” O’Toole said. “They obviously want to be able to spot people who are counting cards and trying to cheat the casino.”
O’Toole collaborated on the research with Dr. P. Jonathon Phillips of the National Institute of Standards and Technology, Dr. Fang Jiang of the University of Washington, and Dr. Abhijit Narvekar of Alcon Labs.
Emily Martinez | EurekAlert!
Construction of practical quantum computers radically simplified
05.12.2016 | University of Sussex
UT professor develops algorithm to improve online mapping of disaster areas
29.11.2016 | University of Tennessee at Knoxville
In recent years, lasers with ultrashort pulses (USP) down to the femtosecond range have become established on an industrial scale. They could advance some applications with the much-lauded “cold ablation” – if that meant they would then achieve more throughput. A new generation of process engineering that will address this issue in particular will be discussed at the “4th UKP Workshop – Ultrafast Laser Technology” in April 2017.
Even back in the 1990s, scientists were comparing materials processing with nanosecond, picosecond and femtosesecond pulses. The result was surprising:...
Have you ever wondered how you see the world? Vision is about photons of light, which are packets of energy, interacting with the atoms or molecules in what...
A multi-institutional research collaboration has created a novel approach for fabricating three-dimensional micro-optics through the shape-defined formation of porous silicon (PSi), with broad impacts in integrated optoelectronics, imaging, and photovoltaics.
Working with colleagues at Stanford and The Dow Chemical Company, researchers at the University of Illinois at Urbana-Champaign fabricated 3-D birefringent...
In experiments with magnetic atoms conducted at extremely low temperatures, scientists have demonstrated a unique phase of matter: The atoms form a new type of quantum liquid or quantum droplet state. These so called quantum droplets may preserve their form in absence of external confinement because of quantum effects. The joint team of experimental physicists from Innsbruck and theoretical physicists from Hannover report on their findings in the journal Physical Review X.
“Our Quantum droplets are in the gas phase but they still drop like a rock,” explains experimental physicist Francesca Ferlaino when talking about the...
The Max Planck Institute for Physics (MPP) is opening up a new research field. A workshop from November 21 - 22, 2016 will mark the start of activities for an innovative axion experiment. Axions are still only purely hypothetical particles. Their detection could solve two fundamental problems in particle physics: What dark matter consists of and why it has not yet been possible to directly observe a CP violation for the strong interaction.
The “MADMAX” project is the MPP’s commitment to axion research. Axions are so far only a theoretical prediction and are difficult to detect: on the one hand,...
16.11.2016 | Event News
01.11.2016 | Event News
14.10.2016 | Event News
07.12.2016 | Health and Medicine
07.12.2016 | Life Sciences
07.12.2016 | Health and Medicine