  We present an unconstrained ear recognition framework that outperforms
state-of-the-art systems in different publicly available image databases. To
this end, we developed CNN-based solutions for ear normalization and
description, we used well-known handcrafted descriptors, and we fused learned
and handcrafted features to improve recognition. We designed a two-stage
landmark detector that successfully worked under untrained scenarios. We used
the results generated to perform a geometric image normalization that boosted
the performance of all evaluated descriptors. Our CNN descriptor outperformed
other CNN-based works in the literature, specially in more difficult scenarios.
The fusion of learned and handcrafted matchers appears to be complementary as
it achieved the best performance in all experiments. The obtained results
outperformed all other reported results for the UERC challenge, which contains
the most difficult database nowadays.
