  Facial wrinkle is one of the most prominent biological changes that
accompanying the natural aging process. However, there are some external
factors contributing to premature wrinkles development, such as sun exposure
and smoking. Clinical studies have shown that heavy smoking causes premature
wrinkles development. However, there is no computerised system that can
automatically assess the facial wrinkles on the whole face. This study
investigates the effect of smoking on facial wrinkling using a social habit
face dataset and an automated computerised computer vision algorithm. The
wrinkles pattern represented in the intensity of 0-255 was first extracted
using a modified Hybrid Hessian Filter. The face was divided into ten
predefined regions, where the wrinkles in each region was extracted. Then the
statistical analysis was performed to analyse which region is effected mainly
by smoking. The result showed that the density of wrinkles for smokers in two
regions around the mouth was significantly higher than the non-smokers, at
p-value of 0.05. Other regions are inconclusive due to lack of large scale
dataset. Finally, the wrinkle was visually compared between smoker and
non-smoker faces by generating a generic 3D face model.
