面向跨数据集指静脉识别的可快速迁移模型  

Fast Transferable Model for Cross-Dataset Finger Vein Recognition

作　　者：黄喆 郭成安 HUANG Zhe;GUO Cheng′an(Faculty of Electronic Information and Electrical Engineering,Dalian University of Technology,Dalian 116024)

机构地区：[1]大连理工大学电子信息与电气工程学部,大连116024

出　　处：《模式识别与人工智能》2023年第8期671-684,共14页Pattern Recognition and Artificial Intelligence

摘　　要：深度学习技术在手指静脉识别任务中展现出显著的性能优势和潜力.但是,由于其昂贵的训练开销以及不同数据集间存在的类别和分布差异,在某个数据集表现优异的模型可能难以高效应用到新数据或者在新数据上表现不佳.针对识别系统被应用到不同使用群体和设备的实际情景,为了实现模型在不同数据上的高效应用并保持其优良性能,文中提出面向跨数据集指静脉识别的可快速迁移模型,包含两个学习阶段的解决方案.首先,为了得到一个可以较好泛化到未见目标数据的深度模型,在第一阶段提出基于特征对齐和聚类的领域适应算法,引导网络提取有判别力且鲁棒的特征.然后,为了减小图像中由偏差场引起的数据集差异,提出一个偏差场校正网络,消除偏差,并调整潜在分布,使其更相似.最后,为了将模型高效迁移到目标数据并充分利用新数据的模版信息,在执行快速迁移的第二阶段中,设计具有更快学习速度的基于改进极限学习机的分类器,利用它的学习算法,加速模型的迁移训练.在四个公开指静脉数据库上的实验表明,文中模型能够在实现高效迁移的同时,取得与在目标任务上进行充分端到端训练的最佳方法同等的识别性能.对于常见的应用场景,能满足实时部署的需求,从而为深度学习技术在跨数据集指静脉识别应用提供一套可行的解决方案.Deep learning-based methods show excellent recognition performance and potential in finger vein recognition.However,due to the expensive training costs and differences in categories and distributions across different datasets,a model that performs well on one dataset may struggle to efficiently adapt to new data or perform poorly on new data.A fast transferable model for cross-dataset finger vein recognition,including a two-stage solution,is proposed to realize high efficient application of model on different datasets with good performance in practical scenarios where recognition systems are applied to various user groups and devices.Firstly,in the first stage,a domain adaptation algorithm based on feature alignment and clustering is introduced to guide the network in extracting discriminative and robust features,aiming to obtain a deep model that can generalize well on unseen target data.Secondly,a bias field correction network is developed to reduce dataset gaps caused by bias fields in images and further adjust the latent distribution to make the datasets more similar to each other.Then,in the second stage of fast transfer,a modified classifier based on extreme learning machine with a faster learning speed is designed to accelerate model transfer training and make full use of the template information of new data.Experimental results on four public finger vein databases show that the proposed method realizes efficient transfer and achieves recognition performance as good as the best end-to-end training method dose in the target task.For common application scenarios,the proposed method can meet the requirements of real-time deployment and provide a feasible solution for the application of deep learning techniques in cross-dataset finger vein recognition.

关 键 词：跨数据集识别 快速迁移学习 指静脉识别 极速学习机(ELM) 两阶段迁移学习 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]