LDM-DAGSVM: Learning Distance Metric via DAG Support Vector Machine for Ear Recognition Problem

Abstract

Recently, the ear recognition system takes more increasingly interesting for many applications, especially, in immigration system, forensic, and surveillance applications. For face re-identification and image classification, metric learning has significantly improved machine learning accuracies by using K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) classifiers. However, metric learning via SVM has not yet been investigated for the ear recognition problem. To achieve better generalization ability than the traditional previous classifiers, a novel framework for ear recognition is proposed based on learning distance metric (LDM) via SVM since the LDM and the directed acyclic graph SVM (DAGSVM) are two emerging techniques which perform outstanding in dealing with classification problems. This work considers metric learning for SVM by proposing a hybrid learning distance metric and directed acyclic graph SVM (LDM-DAGSVM) model for ear recognition system. Different from existing ear biometric methods, the proposed approach aims to learn a Mahalanobis distance metric via SVM to maximize the inter-class variations and minimize the intra-class variations, simultaneously. The experiments are conducted on complicated ear datasets and the results can achieve better performance compared with the state-of-the-art ear recognition methods. The proposed approach can get classification accuracy up to 98.79%, 98.70%, and 84.30% for AWE, AME and WPUT ear datasets, respectively.