Assessment of facial wrinkles as a soft biometrics

Nazre Batool (1) , Sima Taheri (1) , Rama Chellappa (1)

1 University of Maryland Institute for Advanced Computer Studies

This paper presents results on the assessment of facial wrinkles as a soft biometrics. Recently, several micro features such as moles, scars, freckles, etc. have been used in addition to more common facial features for face recognition. The discriminative power of facial wrinkles has not been evaluated. In this paper we present results of our experiments on assessment of discriminative power of wrinkles in recognizing subjects. We treat a set of facial wrinkles from an image as a curve pattern and find similarity between curve patterns from two subjects. Several metrics based on Hausdorff distance and curve-to-curve correspondences are introduced to quantify the similarity. A simple bipartite graph matching algorithm is introduced to find correspondences between curves from two patterns. We present experiments on data sets using manually extracted and automatically detected wrinkles. The recognition rate for these data sets using only the binary forehead wrinkle curve patterns exceeds 65% at rank 1 and 90% at rank 4.

Mots clés: face biometrics; curve pattern; face wrinkles; soft biometrics; bipartite graph matching; Hausdorff distance; wrinkle detection

Domaines: Sciences de l'ingénieur [physics]; Informatique [cs]; Mathématiques [math]

Nazre Batool, Sima Taheri, Rama Chellappa. Assessment of facial wrinkles as a soft biometrics. Porceedings of 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), 2013, IEEE, Apr 2013, Shanghai, China. pp.1 - 7, ⟨10.1109/FG.2013.6553719⟩. ⟨hal-01096615⟩