BIOMETRICS experts have investigated what extent facial aging affects the performance of automatic facial recognition systems. They found that 99 per cent of face images can still be recognized up to 6 years later but that accuracy drops after 6 years if images are not re-taken.
Michigan State University biometrics expert Anil Jain and team set out to investigate what extent facial aging affects the performance of automatic facial recognition systems and what implications it could have on successfully identifying criminals or determining when identification documents need to be renewed.
"We wanted to determine if state-of-the-art facial recognition systems could recognize the same face imaged multiple years apart, such as at age 20 and again at age 30," said Anil Jain, University Distinguished Professor of computer science and engineering. "This is the first study of automatic facial recognition using a statistical model and large longitudinal face database."
Jain and doctoral student Lacey Best-Rowden found that 99 per cent of the face images can still be recognized up to 6 years later.
However, the results also showed that due to natural changes that occur to a face over time as a person ages, recognition accuracy begins to drop if the images of a person were taken more than 6 years apart. This decrease in face recognition accuracy is person-dependent; some people age faster than others due to lifestyle, health conditions, environment or genetics.
Jain's team studied 2 police mugshot databases of repeat criminal offenders with each offender having a minimum of 4 images acquired over at least a five-year period. The total number of repeat offenders studied was 23,600. Mugshot databases are the largest source of facial aging photos available with well-controlled standards to ensure the photos are uniform. These are the largest facial-aging databases studied to date in terms of number of subjects, images per subject and elapsed times. ♦
