Facial Age Estimation is an area of study new to the Face Recognition Vendor Test (FRVT) Still Track. While peripheral to traditional face recognition, it has become a growing area of research given its potential use in commercial and biometric applications. The motivation for age estimation systems has grown in the last few decades given the rise of the digital age and the increase in human- computer interaction. Age-based access control and verification, age-adaptive targeted mar- keting, age-invariant person identification, and age-based indexing of face images are potential applications of automated facial age estimation. NIST performed a large scale empirical evaluation of facial age estimation algorithms, with participation from five com- mercial providers and one university, with three large operational datasets comprised of visa images and law enforcement mughots, leveraging a combined corpus of over 7 million images. Core age estimation accuracy was baselined over a large homogeneous population, then assessed demographically by age group, gender, and ethnicity. The impact of input- driven variations, namely image quality and number of image samples were captured, and assessments of age-verification accuracy and estimation accuracy in children were documented.
Citation: NIST Interagency/Internal Report (NISTIR) - 7995
NIST Pub Series: NIST Interagency/Internal Report (NISTIR)
Pub Type: NIST Pubs
age estimation, biometrics, facial recognition