There has been a continued interest in learning features that are generalizable across sensors and spectra for ocular biometrics. This is usually facilitated through a model that can learn features that are robust across pose, lighting conditions, spectra, and device sensor variations. In this paper, we propose an efficient deep learning-based feature extraction pipeline for learning the aforementioned generalizable features for ocular recognition. The proposed pipeline uses a relatively small Convolutional Neural Network (CNN) based feature extraction model along with a region of interest (ROI) detector and data augmenter. Our proposed CNN model has 36 times fewer parameters compared to the popular ResNet-50. Cross dataset experiments on five benchmark datasets suggest that the proposed feature extraction model, trained only on 200 subjects from the VISOB dataset, reduces the error rate up to 7 × when compared to the existing models.

人们一直对学习功能感兴趣，这些学习功能可在传感器和光谱中用于眼部生物统计信息。通常通过可以学习在姿势，照明条件，光谱和设备传感器变化方面具有鲁棒性的特征的模型来促进这一点。在本文中，我们提出了一种有效的基于深度学习的特征提取管道，用于学习上述用于眼识别的通用特征。拟议中的管线使用相对较小的基于卷积神经网络（CNN）的特征提取模型，以及关注区域（ROI）检测器和数据增强器。与流行的ResNet-50相比，我们提出的CNN模型参数减少了36倍。在五个基准数据集上进行的交叉数据集实验表明，建议的特征提取模型，