This paper investigates the
possibility of exploiting facial skin texture as a source of biometric
information to facilitate automatic recognition of individuals. Such ability
may be particularly important in circumstances when a full view of the face
may not be available.
The proposed algorithm
automatically segments the forehead region and divides it into nonoverlapping
patches. Two state-of-the-art families of texture feature extraction
approaches, namely Gabor wavelet filter and Local Binary Pattern operator,
are compared for extracting features from these patches which are classified
using a k-NN classifier. The identification and verification performance is
evaluated for different patch sizes using the XM2VTS database. For the
verification experiments an EER of 0.065 using Gabor features and 0.083 using
LBP features is obtained for forehead regions with pure skin. Additionally a
novel classifier is presented for automatically detecting pure skin patches
in the forehead region.