Open AccessAssessing facial wrinkles: automatic detection and quantification
Author(s) -
Cula G. O.,
Bargo P. R.,
Nkengne A.,
Kollias N.
Publication year - 2013
Publication title -
skin research and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.521
H-Index - 69
eISSN - 1600-0846
pISSN - 0909-752X
DOI - 10.1111/j.1600-0846.2012.00635.x
Subject(s) - wrinkle , forehead , artificial intelligence , computer science , computer vision , face (sociological concept) , set (abstract data type) , orientation (vector space) , reliability (semiconductor) , pattern recognition (psychology) , mathematics , medicine , surgery , physics , geometry , quantum mechanics , sociology , programming language , gerontology , social science , power (physics)
Background 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. Methods 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. Results 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. Conclusion 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.