A robust contour sampling and tensor-based approach to facial beard and mustache shape segmentation and matching

In this paper, we propose a novel system for beard and mustache segmentation and matching in facial images. We first segment out facial hair contours from the image by utilizing a sparse dictionary on self-quotient images to classify regions as either skin or facial hair. We then landmark the shape contour to obtain points around the contour of the image using a combination of two algorithms, a novel non-uniform sampling algorithm, and points obtained from SIFT. We utilize these landmark points to extract inner distance-based shape context features. Finally, these features are used as inputs for a tensor product graph-based matching system. We run experiments on the Multiple Biometric Grand Challenge (MBGC) and the PINELLAS mugshot databases. Our pipeline achieves 90.3% matching accuracy on a subset of the PINELLAS database when divided into four types of facial hair.