Aging has profound effects on facial biometrics as it causes change in shape and texture.
However, aging remains an under-studied problem in comparison to facial variations
due to pose, illumination and expression changes. A commonly adopted solution
in the state-of-the-art is the virtual template synthesis for aging and de-aging transformations
involving complex 3D modelling techniques. These methods are also prone to
estimation errors in the synthesis. Another viable solution is to continuously adapt the
template to the temporal variation (ageing) of the query data. Though efficacy of template
update procedures has been proven for expression, lightning and pose variations, the use
of template update for facial aging has not received much attention so far. Therefore, this
paper first analyzes the performance of existing baseline facial representations, based on
local features, under ageing effect then investigates the use of template update procedures
for temporal variance due to the facial age progression process. Experimental results on
FGNET and MORPH aging database using commercial VeriLook face recognition engine
demonstrate that continuous template updating is an effective and simple way to adapt to
variations due to the aging process.
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