Recently biometric systems have shown improved capabilities because of the remarkable success of deep learning in solving various computer vision tasks. In ear recognition, the use of deep learning techniques is seldom due to training data scarcity. The existing work has shown poor performance as the majority of techniques are based on either handcraft features or pre-trained models. Besides this, transfer-learning has also shown poor performance because of the diversity among the tasks. To circumvent the existing issues, in this work, we have presented an end-to-end framework for ear recognition. It consist of the Ear Mask Extraction (EME) network to segment the ear, a normalization algorithm to align the ear, and a novel siamese-based CNN (CG-ERNet) for deep ear feature learning. CG-ERNet exploits domain-specific knowledge by using Curvature Gabor filters and uses triplet loss, triplet selection, and adaptive margin for better convergence of the loss. For comparative analysis, we trained state-of-the-art deep learning models like Face-Net, VGG19, ResNet50, Inception, Exception, and Mobile-Net for ear-recognition. The performance is assessed using five well-known evaluation metrics. In the extensive experimentation, our proposed model (CG-ERNet) outperformed the deep learning models and handcrafted feature based methods on four different, publicly available, benchmark datasets. To make the results more interpretable, we employ the t-SNE visualization of learned features. Additionally, our proposed method has shown robustness to various environmental challenges like Gaussian noise, Gaussian blur, up to ± 30 degrees of rotation, and 20% of occlusion.

最近，由于深度学习在解决各种计算机视觉任务方面取得了巨大的成功，因此生物识别系统已显示出改进的功能。在耳朵识别中，由于训练数据稀缺，很少使用深度学习技术。现有工作显示出较差的性能，因为大多数技术都基于手工功能或预先训练的模型。除此之外，由于任务之间的多样性，转移学习的表现也很差。为了规避现有问题，在这项工作中，我们提出了端到端的人耳识别框架。它由用于分割耳朵的耳罩提取（EME）网络，用于对齐耳朵的归一化算法以及用于深度耳特征学习的新型基于暹罗的CNN（CG-ERNet）组成。CG-ERNet通过使用曲率Gabor滤波器来利用特定领域的知识，并使用三重态损失，三重态选择和自适应余量来更好地收敛损失。为了进行比较分析，我们训练了最先进的深度学习模型，例如Face-Net，VGG19，ResNet50，Inception，Exception和Mobile-Net来进行人耳识别。使用五个众所周知的评估指标来评估性能。在广泛的实验中，我们提出的模型（CG-ERNet）在四个不同的，公开可用的基准数据集上优于深度学习模型和基于手工特征的方法。为了使结果更易于解释，我们使用t-SNE可视化所学特征。此外，我们提出的方法已显示出对各种环境挑战的鲁棒性，例如高斯噪声，高斯模糊，