Metric Learning with Dynamically Generated Pairwise Constraints for Ear Recognition

TL;DR

This paper introduces a novel metric learning approach for ear recognition that dynamically generates pairwise constraints, improving recognition accuracy and training efficiency compared to existing methods.

Contribution

The paper proposes a new metric learning method using pairwise constraints and iterated Bregman projections, specifically tailored for ear recognition tasks.

Findings

Achieves promising recognition rates on multiple datasets.

Training process is more efficient than existing metric learning methods.

Effective in distinguishing between same and different ears.

Abstract

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.