Metric Learning with Dynamically Generated Pairwise Constraints for Ear Recognition
This work addresses ear recognition for biometric identification, presenting an incremental improvement in training efficiency for a specific domain.
The paper tackles ear recognition by predicting if two ear images are from the same person, proposing a metric learning method that dynamically generates pairwise constraints and uses iterated Bregman projections, achieving promising recognition rates and more efficient training than other methods on AMI, USTB II, and WPUT databases.
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.