Facial Biometric Templates and Aging: Problems and Challenges for Artificial Intelligence
Date Issued
2009
Author(s)
Abstract
The performance of face recognition and/or authentication systems is
greatly affected by within-person variations encountered in human faces. Within
person facial variations distort the appearance of faces leading to inconsistencies
between facial features stored in templates and features derived from a face, of the
corresponding subject, captured at a different instance. For this reason a considerable
amount of effort has been devoted to the development of methods for eliminating
within person-variations during face recognition/authentication. Among all
types of within-person variations encountered, aging-related variations display
unique characteristics that make the process of dealing with this type of variation a
challenging task. In this paper we describe experiments that enable the quantification
of the effects of aging on the performance of face recognition systems. We also
review typical approaches that aim to eliminate the effects of aging in face recognition
and outline future research directions for this area.
greatly affected by within-person variations encountered in human faces. Within
person facial variations distort the appearance of faces leading to inconsistencies
between facial features stored in templates and features derived from a face, of the
corresponding subject, captured at a different instance. For this reason a considerable
amount of effort has been devoted to the development of methods for eliminating
within person-variations during face recognition/authentication. Among all
types of within-person variations encountered, aging-related variations display
unique characteristics that make the process of dealing with this type of variation a
challenging task. In this paper we describe experiments that enable the quantification
of the effects of aging on the performance of face recognition systems. We also
review typical approaches that aim to eliminate the effects of aging in face recognition
and outline future research directions for this area.