Recently, deep convolutional neural networks (CNNs) have been used for ear recognition with the increasing and available ear image databases. However, most known ear recognition methods may be affected by selecting and weighting features; this is always a challenging issue in ear recognition and other pattern recognition applications. Metric learning can address this issue by using an accurate and efficient metric distance called Mahalanobis distance. Therefore, this paper presents a novel approach for ear recognition problems based on a learning Mahalanobis distance metric on deep CNN features. In detail, firstly, various deep features are extracted by adopting VGG and ResNet pre-trained models. Secondly, the discriminant correlation analysis is exploited to eliminate the dimensionality problem. Thirdly, the Mahalanobis distance is learned based on LogDet divergence metric learning. Finally, K-nearest neighbor is used for ear recognition. The experiments are performed on four public ear databases: AWE, USTB II, AMI, and WPUT, and experimental results prove that the proposed approach outperforms the existing state-of-the-art ear recognition methods.

最近，随着越来越多的可用耳朵图像数据库，深度卷积神经网络（CNN）已用于耳朵识别。但是，大多数已知的耳朵识别方法可能会受到特征选择和加权的影响。在耳朵识别和其他模式识别应用中，这始终是一个具有挑战性的问题。公制学习可以通过使用准确有效的公制距离（称为Mahalanobis距离）来解决此问题。因此，本文提出了一种基于深度CNN特征的学习Mahalanobis距离度量的人耳识别问题的新方法。详细地说，首先，采用VGG和ResNet预训练模型提取各种深度特征。其次，利用判别相关分析消除维数问题。第三，马氏距离是根据LogDet发散度量学习而学习的。最后，K近邻用于耳朵识别。实验是在四个公共耳朵数据库上进行的：AWE，USTB II，AMI和WPUT，实验结果证明，该方法优于现有的最新耳朵识别方法。