  This paper proposes a hierarchical multi-label matcher for patch-based face
recognition. In signature generation, a face image is iteratively divided into
multi-level patches. Two different types of patch divisions and signatures are
introduced for 2D facial image and texture-lifted image, respectively. The
matcher training consists of three steps. First, local classifiers are built to
learn the local matching of each patch. Second, the hierarchical relationships
defined between local patches are used to learn the global matching of each
patch. Three ways are introduced to learn the global matching: majority voting,
l1-regularized weighting, and decision rule. Last, the global matchings of
different levels are combined as the final matching. Experimental results on
different face recognition tasks demonstrate the effectiveness of the proposed
matcher at the cost of gallery generalization. Compared with the UR2D system,
the proposed matcher improves the Rank-1 accuracy significantly by 3% and 0.18%
on the UHDB31 dataset and IJB-A dataset, respectively.
