  Ear recognition task is known as predicting whether two ear images belong to
the same person or not. In this paper, we present a novel metric learning
method for ear recognition. This method is formulated as a pairwise constrained
optimization problem. In each training cycle, this method selects the nearest
similar and dissimilar neighbors of each sample to construct the pairwise
constraints, and then solve the optimization problem by the iterated Bregman
projections. Experiments are conducted on AMI, USTB II and WPUT databases. The
results show that the proposed approach can achieve promising recognition rates
in ear recognition, and its training process is much more efficient than the
other competing metric learning methods.
