FV-MViT:手机视觉变压器用于手指静脉识别
Xiongjun Li1, Jin Feng1, Jilin Cai1
1College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China.
Sensors (Basel, Switzerland)
|February 24, 2024
概括
该FV-MViT型号提供准确和高效的手指静脉识别与轻量级的设计. 它克服了高参数和有限数据的挑战,实现了生物识别安全的卓越性能.
科学领域:
- 生物识别和模式识别技术
- 对于安全应用程序的深度学习
- 计算机视觉和机器学习
背景情况:
- 高参数数量和有限的培训数据在开发准确的指纹静脉识别系统方面带来了重大挑战.
- 现有的深度学习模型往往在有效性,可移植性和低延迟要求方面扎,以满足现实世界生物识别应用的需求.
研究的目的:
- 介绍FV-MViT,这是一种新的轻量级深度学习模型,旨在实现高精度,便携和低延迟的手指静脉识别.
- 通过减少参数数量和特征提取时间来解决现有方法的局限性,同时保持卓越的识别性能.
主要方法:
- FV-MViT模型包含一个Mul-MV2块,具有多尺度卷积,用于增强局部特征提取.
- 它具有增强的MobileViT块,优化了变压器对线性复杂性的自我注意力,并使用深度可分离的卷积来有效地提取全局特征.
- 使用软目标中心交叉损失函数来提高模型的概括性和准确性.
主要成果:
- 该FV-MViT模型实现了99.53% (SDU数据集) 和100.00% (USM数据集) 的高识别精度.
- 记录了0.47% (SDU) 和0.02% (USM) 的异常等错率 (EER).
- 该模型显示了低参数数量 (5.26万) 和最小的延迟时间 (10.00毫秒),超过了最先进的方法.
结论:
- FV-MViT模型为指纹静脉识别提供了高效和高效的解决方案,适用于实际的生物识别系统.
- 它的轻量级架构与新的卷积和注意力机制相结合,可以显著降低计算开销,而不会影响准确性.
- 该模型的性能表明它有可能在安全和便携式身份验证应用中得到广泛采用.
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