我们设计一个新奇的规则化框架以学习相似性度量用于无约束人脸验证。我们形式化它的目标函数通过融合鲁棒性对于大规模的个人人脸的内部变化和新奇的相似性度量的辨别力。额外，我们的形式是一个凸优化问题，保证了全局最优解的存在。-we migrate such a geometric

model to address face recognition and verification together

through proposing a unified archetype hull ranking fr a mework. Upon a scalable graph characterized by a compact

set of archetype exemplars whose convex hull encompasses

most of the training images, the proposed fr a mework 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.