Facial Skin Texture as a Source of Biometric Information

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.