  Face verification can be regarded as a 2-class fine-grained visual
recognition problem. Enhancing the feature's discriminative power is one of the
key problems to improve its performance. Metric learning technology is often
applied to address this need, while achieving a good tradeoff between
underfitting and overfitting plays the vital role in metric learning. Hence, we
propose a novel ensemble cascade metric learning (ECML) mechanism. In
particular, hierarchical metric learning is executed in the cascade way to
alleviate underfitting. Meanwhile, at each learning level, the features are
split into non-overlapping groups. Then, metric learning is executed among the
feature groups in the ensemble manner to resist overfitting. Considering the
feature distribution characteristics of faces, a robust Mahalanobis metric
learning method (RMML) with closed-form solution is additionally proposed. It
can avoid the computation failure issue on inverse matrix faced by some
well-known metric learning approaches (e.g., KISSME). Embedding RMML into the
proposed ECML mechanism, our metric learning paradigm (EC-RMML) can run in the
one-pass learning manner. Experimental results demonstrate that EC-RMML is
superior to state-of-the-art metric learning methods for face verification.
And, the proposed ensemble cascade metric learning mechanism is also applicable
to other metric learning approaches.
