基于深度学习的眼周识别方法研究

PERIOCULAR RECOGNITION APPROACH BASED ON DEEP LEARNING

为了提高眼周识别性能,提出一种基于深度卷积神经网络的眼周识别方法(PeriocularNet)。PeriocularNet具有16层卷积神经网络,融入了残差学习模块,使用了ArcFace损失函数;在训练策略上引入数据增强,以解决训练过程中产生的过拟合。在UBIPr、UBIRIS.V2数据集上进行实验,实验结果表明所提方法的识别EER值分别达到1.9%和7.9%,相较于经典的眼周识别方法取得了更好的眼周识别性能。另外,为了验证端到端的眼周识别方法中眉毛区域特征对眼周识别性能的影响,建立两个涉及三种眉毛形态的眼周数据集。通过实验验证,保持眉毛区域特征不变的眼周数据识别EER比其他两种去掉眉毛特征的情况更低,表明眉毛区域特征能够提高眼周识别性能。

In order to improve the performance of periocular recognition,a new method based on deep convolutional neural networks referred to as PeriocularNet is proposed.PeriocularNet exploited a 16-layer convolutional neural network,integrated with a residual learning module,and adopted the ArcFace loss function.Data augmentation was introduced to avoid the over-fitting in training process.The experiments on UBIPr and UBIRIS.V2 datasets show that the equal error rate(EER)of the proposed approach achieve 1.9%and 7.9%respectively.which improves the periocular recognition performance compared to the related methods.In addition,in order to verify the effect of the eyebrow region feature on the performance of periocular recognition in the end-to-end approach,two periocular datasets,UBIPr-1 and UBIRIS-1,involving three eyebrow shapes were established.Experimental results show that the EER of images containing the eyebrow feature is lower than that of the eyebrow feature removed,which indicates the importance of eyebrow feature in periocular recognition.