FV-EffResNet:一种高效的轻型卷积神经网络,用于识别手指静脉
Yusuf Suleiman Tahir1, Bakhtiar Affendi Rosdi1
1School of Electrical and Electronic Engineering, Universiti Sains Malaysia, Nibong Tebal, Penang, Malaysia.
PeerJ. Computer science
|March 4, 2024
概括
一个新的轻量级卷积神经网络 (CNN),FV-EffResNet,提供高效的手指静脉识别. 它平衡网络大小,速度和准确性,使其适合实时应用.
科学领域:
- 生物识别信息 生物识别信息
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 深度神经网络具有先进的手指静脉识别功能,但通常需要高计算资源.
- 当前最先进的模型复杂且计算成本昂贵,限制了实时和嵌入式应用程序.
研究的目的:
- 开发一个轻量级的卷积神经网络 (CNN) 用于手指静脉识别.
- 为了实现实际实施,在网络尺寸,速度和准确性之间实现平衡.
主要方法:
- 介绍了FV-EffResNet,一种新的轻量级CNN架构.
- 提出了一个高效剩余 (EffRes) 块,使用具有矩形尺寸的分解卷积 (点向和深度向).
- 利用压缩单元,深度卷积,聚合策略,Swish激活和循环学习率来提高效率和性能.
主要成果:
- 阻断EffRes显著改善了手指静脉识别性能.
- FV-EffResNet在识别和验证任务中展示了最先进的结果.
- 该网络实现了高精度,降低了计算成本和复杂性.
结论:
- 拟议的FV-EffResNet提供了一种有效和高效的解决方案,用于指纹静脉识别.
- 轻量级设计和新的EffRes块使嵌入式系统上的实用实时应用成为可能.
相关概念视频
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IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the C=O, C=N, and C=C occur between 1600–1850 cm−1.
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The deep venous system is primarily composed of the ulnar and radial veins. The ulnar vein, which drains the fingers through the superficial palmar venous arches, and the radial vein, which serves the palms via the deep palmar...