The DOI matches its Crossref record.
Modeling Age Progression in Young Faces
Abstract
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.
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Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
These authors usually assess the accuracy of their algorithms based on mathematical similarity between an age-progressed image and a target individual.
“These authors usually assess the accuracy of their algorithms based on mathematical similarity between an age-progressed image and a target individual.”
From Simulation of facial growth based on longitudinal data: Age progression and age regression between 7 and 17 years of age using 3D surface data (2019) · cites this paper as [46]
Numerous studies dealing with facial age progression have recently been published, mostly by computer vision scientists.
“Numerous studies dealing with facial age progression have recently been published, mostly by computer vision scientists [13,43–46].”
From Simulation of facial growth based on longitudinal data: Age progression and age regression between 7 and 17 years of age using 3D surface data (2019) · cites this paper as [46]
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Research works printed but not linked: 24
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Cited by
Sorted by citations
Scheuerman, M. K., Wade, K., Lustig, C., & Brubaker, J. R. (2020) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3392866 · Cited by 193
Koudelová, J., Hoffmannová, E., Dupej, J., & Velemínská, J. (2019) · PLOS ONE · doi:10.1371/journal.pone.0212618 · Cited by 33
Child Face Age-Progression via Deep Feature Aging
Deb, D., Aggarwal, D., & Jain, A. K. (2020) · arXiv · doi:10.48550/arxiv.2003.08788 · Cited by 4
Metadata
- Authors
- Ramanathan, N., & Chellappa, R.
- Venue
- 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1 (CVPR'06)
- Publisher
- IEEE
- Published
- 2006
- Volume
- 1
- Pages
- 387-394
- Cited by
- 239
- References
- 0
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