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Facial age synthesis using sparse partial least squares (the case of Ben Needham)
Bukar, Ali M. Ugail, Hassan
Bukar, Ali M.
Ugail, Hassan
Publication Date
2017-09
End of Embargo
Supervisor
Rights
© 2017 American Academy of Forensic Sciences.
This is the peer reviewed version of the following article: Bukar AM and Ugail H (2017) Facial age
synthesis using sparse partial least squares (the case of Ben Needham). Journal of Forensic
Sciences. 62(5): 1205-1212, which has been published in final form at
[http://dx.doi.org/10.1111/1556-4029.13523]. This article may be used for non-commercial
purposes in accordance with Wiley Terms and Conditions for Self-Archiving.
Peer-Reviewed
Yes
Open Access status
Accepted for publication
2017-11-28
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Department
Awarded
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Additional title
Abstract
Automatic facial age progression (AFAP) has been an active area of research in recent years.
This is due to its numerous applications which include searching for missing. This study
presents a new method of AFAP. Here, we use an Active Appearance Model (AAM) to extract
facial features from available images. An ageing function is then modelled using Sparse Partial
Least Squares Regression (sPLS). Thereafter, the ageing function is used to render new faces at
different ages. To test the accuracy of our algorithm, extensive evaluation is conducted using a
database of 500 face images with known ages. Furthermore, the algorithm is used to progress
Ben Needham’s facial image that was taken when he was 21 months old to the ages of 6, 14 and
22 years. The algorithm presented in this paper could potentially be used to enhance the search
for missing people worldwide.
Version
Accepted Manuscript
Citation
Bukar AM and Ugail H (2017) Facial age synthesis using sparse partial least squares (the
case of Ben Needham). Journal of Forensic Sciences. 62(5): 1205-1212.
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Article