Assessing facial wrinkles: automatic detection and quantification
Article first published online: 12 JUN 2012
© 2012 John Wiley & Sons A/S
Skin Research and Technology
Volume 19, Issue 1, pages e243–e251, February 2013
How to Cite
Cula, G. O., Bargo, P. R., Nkengne, A. and Kollias, N. (2013), Assessing facial wrinkles: automatic detection and quantification. Skin Research and Technology, 19: e243–e251. doi: 10.1111/j.1600-0846.2012.00635.x
- Issue published online: 7 JAN 2013
- Article first published online: 12 JUN 2012
- Manuscript Accepted: 26 APR 2012
- facial wrinkles;
- automatic detection;
As people mature, their skin gradually presents lines, wrinkles, and folds that become more pronounced with time. Skin wrinkles are perceived as important cues in communicating information about the age of the person. Nowadays, documenting the facial appearance through imaging is prevalent in skin research, therefore detection and quantitative assessment of the degree of facial wrinkling can be a useful tool for establishing an objective baseline and for assessing benefits to facial appearance due to various dermatological treatments. However, few image-based algorithms for computationally assessing facial wrinkles are present in the literature, and those that exist have limited reliability.
In this work, an algorithm for automatic detection of facial wrinkles is developed, based on estimating the orientation and the frequency of elongated spatial features, captured via digital image filtering.
The algorithm is tested against one set of clinically validated 11-point wrinkle scales present on the face. The algorithm is employed for assessing the presence of forehead furrows on a set of 100 clinically graded facial images. The proposed computational assessment correlates well with the corresponding clinical scores.
We find that the results are in better agreement with clinical scoring when the wrinkle depth information, approximated via filter responses, is combined with the wrinkle length information as opposed to the case when the two measures are considered separately.