Bai Haohan, Tan Yubo, Li Yong-Jie
Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China (UESTC), Huzhou 313001, China.
School of Life Science and Technology, UESTC, Chengdu 610054, China.
Biomed Opt Express. 2024 Nov 15;15(12):6845-6863. doi: 10.1364/BOE.535390. eCollection 2024 Dec 1.
The problems of complex background, low quality of finger vein images, and poor discriminative features have been the bottleneck of feature extraction and finger vein recognition. To this end, we propose a feature extraction algorithm based on the open-set testing protocol. In order to eliminate the interference of irrelevant areas, this paper proposes the idea of segmentation-assisted classification, that is, using the rough mask of the finger vein to constrain the feature learning process so that the network can focus on the vein area and learn greater weight for the vein. Specifically, the feature maps of the shallow layers of the network are first sent to the feature pyramid module to fuse the primary features of different scales, which are then sent to the spatial attention module to obtain the spatial weight map of the image. Based on the results of several classical vein skeleton extraction algorithms, a weighting method is used to obtain a more accurate mask to constrain the learning of the spatial weight map. Finally, a hybrid loss function combining triplet loss and cross-entropy loss is used to reduce the distance between feature vectors of the same categories and increase the distance between feature vectors of different categories in the Euclidean space, thereby improving feature discriminability. Good recognition results were achieved on the three public data sets of SDUMLA, MMCBNU, and FVUSM, and the values of equal error rate (EER) on them are as low as 2.50%, 0.20%, and 0.14%, respectively.
复杂背景、手指静脉图像质量低以及判别特征差等问题一直是特征提取和手指静脉识别的瓶颈。为此，我们提出了一种基于开放集测试协议的特征提取算法。为了消除无关区域的干扰，本文提出了分割辅助分类的思想，即利用手指静脉的粗糙掩码来约束特征学习过程，使网络能够专注于静脉区域并为静脉学习更大的权重。具体来说，首先将网络浅层的特征图发送到特征金字塔模块以融合不同尺度的初级特征，然后将其发送到空间注意力模块以获得图像的空间权重图。基于几种经典静脉骨架提取算法的结果，采用加权方法获得更准确的掩码来约束空间权重图的学习。最后，使用结合三元组损失和交叉熵损失的混合损失函数来减小欧几里得空间中同类特征向量之间距离，并增大不同类特征向量之间的距离，从而提高特征的可辨别性。在SDUMLA、MMCBNU和FVUSM这三个公共数据集上取得了良好的识别结果，它们的等错误率（EER）值分别低至2.50%、0.20%和0.14%。
