A multi-layer model for face aging simulation
Transactions on edutainment VI
Some issues of biometrics: technology intelligence, progress and challenges
International Journal of Information Technology and Management
A novel statistical model to evaluate the performance of EBGM based face recognition
PerMIn'12 Proceedings of the First Indo-Japan conference on Perception and Machine Intelligence
Age invariant face verification with relative craniofacial growth model
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
How does aging affect facial components?
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume 2
Pattern Recognition Letters
Speaker verification in score-ageing-quality classification space
Computer Speech and Language
Local descriptors in application to the aging problem in face recognition
Pattern Recognition
ICIC'13 Proceedings of the 9th international conference on Intelligent Computing Theories
Age-Invariant face recognition using shape transformation
ICIC'13 Proceedings of the 9th international conference on Intelligent Computing Theories
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One of the challenges in automatic face recognition is to achieve temporal invariance. In other words, the goal is to come up with a representation and matching scheme that is robust to changes due to facial aging. Facial aging is a complex process that affects both the 3D shape of the face and its texture (e.g., wrinkles). These shape and texture changes degrade the performance of automatic face recognition systems. However, facial aging has not received substantial attention compared to other facial variations due to pose, lighting, and expression. We propose a 3D aging modeling technique and show how it can be used to compensate for the age variations to improve the face recognition performance. The aging modeling technique adapts view-invariant 3D face models to the given 2D face aging database. The proposed approach is evaluated on three different databases (i.g., FG-NET, MORPH, and BROWNS) using FaceVACS, a state-of-the-art commercial face recognition engine.