  Recently, the face recognizers based on linear representations have been
shown to deliver state-of-the-art performance. In real-world applications,
however, face images usually suffer from expressions, disguises and random
occlusions. The problematic facial parts undermine the validity of the
linear-subspace assumption and thus the recognition performance deteriorates
significantly. In this work, we address the problem in a
learning-inference-mixed fashion. By observing that the linear-subspace
assumption is more reliable on certain face patches rather than on the holistic
face, some Bayesian Patch Representations (BPRs) are randomly generated and
interpreted according to the Bayes' theory. We then train an ensemble model
over the patch-representations by minimizing the empirical risk w.r.t the
"leave-one-out margins". The obtained model is termed Optimal Representation
Ensemble (ORE), since it guarantees the optimality from the perspective of
Empirical Risk Minimization. To handle the unknown patterns in test faces, a
robust version of BPR is proposed by taking the non-face category into
consideration. Equipped with the Robust-BPRs, the inference ability of ORE is
increased dramatically and several record-breaking accuracies (99.9% on Yale-B
and 99.5% on AR) and desirable efficiencies (below 20 ms per face in Matlab)
are achieved. It also overwhelms other modular heuristics on the faces with
random occlusions, extreme expressions and disguises. Furthermore, to
accommodate immense BPRs sets, a boosting-like algorithm is also derived. The
boosted model, a.k.a Boosted-ORE, obtains similar performance to its prototype.
Besides the empirical superiorities, two desirable features of the proposed
methods, namely, the training-determined model-selection and the
data-weight-free boosting procedure, are also theoretically verified.
