Employing Fusion of Learned and Handcrafted Features for Unconstrained Ear Recognition

TL;DR

This paper introduces a novel unconstrained ear recognition framework that combines CNN-based learned features with handcrafted descriptors, achieving superior performance on challenging datasets and surpassing state-of-the-art methods.

Contribution

The study develops a two-stage landmark detector and fuses learned and handcrafted features, significantly improving ear recognition accuracy in unconstrained scenarios.

Findings

CNN descriptor outperforms existing CNN methods in difficult scenarios

Fusion of learned and handcrafted features yields the best recognition performance

Achieved top results on the challenging UERC database

Abstract

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.