Description: we migrate such a geometric
model to address face recognition and verification together
through proposing a unified archetype hull ranking framework. Upon a scalable graph characterized by a compact
set of archetype exemplars whose convex hull encompasses
most of the training images, the proposed framework explicitly captures the relevance between any query and the
stored archetypes, yielding a rank vector over the archetype
hull. The archetype hull ranking is then d on every block of face images to generate a blockwise similarity
measure that is achieved by comparing two different rank
vectors with respect to the same archetype hull. After integrating blockwise similarity measurements with learned importance weights, we accomplish a sensible face similarity
measure which can support robust and effective face recognition and verification.
To Search:
File list (Check if you may need any files):
Face Recognition via Archetype Hull Ranking_via_2013_ICCV_new1.pdf
Download