Hsia Chih-Hsien, Ke Liang-Ying, Chen Sheng-Tao
Department of Computer Science and Information Engineering, National Ilan University, Yilan County 26047, Taiwan.
Department of Business Administration, Chaoyang University of Technology, Taichung City 413310, Taiwan.
Bioengineering (Basel). 2023 Aug 3;10(8):919. doi: 10.3390/bioengineering10080919.
Computer vision (CV) technology and convolutional neural networks (CNNs) demonstrate superior feature extraction capabilities in the field of bioengineering. However, during the capturing process of finger-vein images, translation can cause a decline in the accuracy rate of the model, making it challenging to apply CNNs to real-time and highly accurate finger-vein recognition in various real-world environments. Moreover, despite CNNs' high accuracy, CNNs require many parameters, and existing research has confirmed their lack of shift-invariant features. Based on these considerations, this study introduces an improved lightweight convolutional neural network (ILCNN) for finger vein recognition. The proposed model incorporates a diverse branch block (DBB), adaptive polyphase sampling (APS), and coordinate attention mechanism (CoAM) with the aim of improving the model's performance in accurately identifying finger vein features. To evaluate the effectiveness of the model in finger vein recognition, we employed the finger-vein by university sains malaysia (FV-USM) and PLUSVein dorsal-palmar finger-vein (PLUSVein-FV3) public database for analysis and comparative evaluation with recent research methodologies. The experimental results indicate that the finger vein recognition model proposed in this study achieves an impressive recognition accuracy rate of 99.82% and 95.90% on the FV-USM and PLUSVein-FV3 public databases, respectively, while utilizing just 1.23 million parameters. Moreover, compared to the finger vein recognition approaches proposed in previous studies, the ILCNN introduced in this work demonstrated superior performance.
计算机视觉（CV）技术和卷积神经网络（CNN）在生物工程领域展现出卓越的特征提取能力。然而，在手指静脉图像的采集过程中，平移会导致模型准确率下降，使得在各种现实环境中将CNN应用于实时且高精度的手指静脉识别具有挑战性。此外，尽管CNN准确率很高，但它需要许多参数，并且现有研究已证实其缺乏平移不变特征。基于这些考虑，本研究引入了一种用于手指静脉识别的改进型轻量级卷积神经网络（ILCNN）。所提出的模型结合了多样分支块（DBB）、自适应多相采样（APS）和坐标注意力机制（CoAM），旨在提高模型在准确识别手指静脉特征方面的性能。为了评估该模型在手指静脉识别中的有效性，我们采用了马来西亚理科大学手指静脉（FV-USM）和PLUSVein背掌手指静脉（PLUSVein-FV3）公共数据库，以便与近期的研究方法进行分析和比较评估。实验结果表明，本研究提出的手指静脉识别模型在FV-USM和PLUSVein-FV3公共数据库上分别实现了令人印象深刻的99.82%和95.90%的识别准确率，同时仅使用了123万个参数。此外，与先前研究中提出的手指静脉识别方法相比，本工作中引入的ILCNN表现出更优的性能。
