TY - GEN
T1 - Modeling age progression in young faces
AU - Ramanathan, Narayanan
AU - Chellappa, Rama
PY - 2006
Y1 - 2006
N2 - We propose a craniofacial growth model that characterizes growth related shape variations observed in human faces during formative years. The model draws inspiration from the 'revised' cardioidal strain transformation model proposed in psychophysical studies related to craniofacial growth. The model takes into account anthropometric evidences collected on facial growth and hence is in accordance with the observed growth patterns in human faces across years. We characterize facial growth by means of growth parameters defined over facial landmarks often used in anthropometric studies. We illustrate how the age-based anthropometric constraints on facial proportions translate into linear and non-linear constraints on facial growth parameters and propose methods to compute the optimal growth parameters. The proposed craniofacial growth model can be used to predict one's appearance across years and to perform face recognition across age progression. This is demonstrated on a database of age separated face images of individuals under 18 years of age.
AB - We propose a craniofacial growth model that characterizes growth related shape variations observed in human faces during formative years. The model draws inspiration from the 'revised' cardioidal strain transformation model proposed in psychophysical studies related to craniofacial growth. The model takes into account anthropometric evidences collected on facial growth and hence is in accordance with the observed growth patterns in human faces across years. We characterize facial growth by means of growth parameters defined over facial landmarks often used in anthropometric studies. We illustrate how the age-based anthropometric constraints on facial proportions translate into linear and non-linear constraints on facial growth parameters and propose methods to compute the optimal growth parameters. The proposed craniofacial growth model can be used to predict one's appearance across years and to perform face recognition across age progression. This is demonstrated on a database of age separated face images of individuals under 18 years of age.
UR - https://www.scopus.com/pages/publications/33845578024
UR - https://www.scopus.com/pages/publications/33845578024#tab=citedBy
U2 - 10.1109/CVPR.2006.187
DO - 10.1109/CVPR.2006.187
M3 - Conference contribution
AN - SCOPUS:33845578024
SN - 0769525970
SN - 9780769525976
T3 - Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
SP - 387
EP - 394
BT - Proceedings - 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2006
T2 - 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2006
Y2 - 17 June 2006 through 22 June 2006
ER -