Employing Fusion of Learned and Handcrafted Features for Unconstrained Ear Recognition
This work addresses ear recognition in challenging, unconstrained environments, which is an incremental improvement for biometric security applications.
The paper tackled unconstrained ear recognition by developing a framework that fuses learned and handcrafted features, achieving state-of-the-art performance on public databases, including outperforming all reported results on the UERC challenge.
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.