Automated Assessment of Facial Wrinkling: a case study on the effect of smoking

TL;DR

This study presents an automated computer vision system to assess facial wrinkles and investigates the impact of smoking, revealing significantly higher wrinkle density around the mouth in smokers compared to non-smokers.

Contribution

It introduces a novel automated method for facial wrinkle assessment and provides evidence of smoking's localized effect on wrinkle development.

Findings

Higher wrinkle density around the mouth in smokers

Automated wrinkle detection using Hybrid Hessian Filter

Significant statistical difference at p-value 0.05

Abstract

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.