改进的轻量级卷积神经网络用于手指静脉识别系统
Chih-Hsien Hsia1,2, Liang-Ying Ke1, Sheng-Tao Chen3
1Department of Computer Science and Information Engineering, National Ilan University, Yilan County 26047, Taiwan.
Bioengineering (Basel, Switzerland)
|August 26, 2023
概括
这项研究引入了一种改进的轻型卷积神经网络 (ILCNN),用于准确识别手指静脉. 新型模型增强了特征提取,实现了高精度,更少的参数用于现实世界的应用.
科学领域:
- 生物工程是生物工程.
- 计算机视觉 计算机视觉
- 生物识别信息 生物识别信息
背景情况:
- 计算机视觉 (CV) 和卷积神经网络 (CNN) 在生物工程中的特征提取方面表现出色.
- 传统的CNN在手指静脉成像中难以翻译,阻碍了实时,准确的识别.
- CNNs拥有众多参数,缺乏固有的转移不变特征,限制了它们的实际应用.
研究的目的:
- 开发一个改进的轻量级卷积神经网络 (ILCNN),用于增强手指静脉识别.
- 解决现有的CNN在处理翻译和生物识别参数效率方面的局限性.
- 提高指纹静脉识别系统在不同环境中的准确性和稳定性.
主要方法:
- 引入了一种改进的轻质卷积神经网络 (ILCNN),包含多样化的分支区块 (DBB).
- 集成自适应多相采样 (APS) 和协调注意力机制 (CoAM),以促进特征识别.
- 马来西亚大学科学院 (FV-USM) 和PLUSVein背手掌指脉 (PLUSVein-FV3) 公共数据库使用手指脉的评估.
主要成果:
- ILCNN模型在FV-USM上实现了99.82%的高识别准确率,在PLUSVein-FV3.3上达到95.90%.
- 拟议的模型使用了显著减少的参数数量,仅为123万.
- 对比分析表明,ILCNN的性能优于以前的手指静脉识别方法.
结论:
- 开发的ILCNN为指纹静脉识别提供了高度准确和参数高效的解决方案.
- 整合DBB,APS和CoAM有效地提高了模型捕获歧视性手指静脉特征的能力.
- 这项研究为实时和可靠的生物识别系统带来了有前途的进展.
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