Facial age synthesis using sparse partial least squares (the case of Ben Needham)
End of embargo2018-06-06
MetadataView full catalogue record
KeywordsForensic science; Age estimation; Age synthesis; Active appearance model; Sparse partial least squares regression; Age progression; Ben Needham
Permissions© 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.
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.