用手指在屏幕上画数字实现身份认证，准确率达89%。
Neural Network-Powered Finger-Drawn Biometric Authentication
- 用CNN和自编码器分析手指画的数字图案进行认证
- 两种CNN模型准确率均达89%，轻量模型参数更少
- 适合移动端使用，可与现有密码系统结合
本文研究基于神经网络的触屏设备指纹绘制生物识别认证方法。通过用户在个人触屏设备上绘制0-9数字图案，评估CNN与自编码器架构的认证效果。20名参与者每人贡献2000个绘制样本。对比了改进的Inception-V1网络与轻量级浅层CNN，二者认证准确率均约89%，其中浅层CNN参数更少。同时测试卷积与全连接自编码器用于异常检测，准确率约75%。结果表明，手指绘制符号认证是一种可行、安全且用户友好的触屏生物识别方案，可与现有图形密码系统结合，构建多层移动安全体系。
原文摘要 · Abstract (English)
This paper investigates neural network-based biometric authentication using finger-drawn digits on touchscreen devices. We evaluated CNN and autoencoder architectures for user authentication through simple digit patterns (0-9) traced with finger input. Twenty participants contributed 2,000 finger-drawn digits each on personal touchscreen devices. We compared two CNN architectures: a modified Inception-V1 network and a lightweight shallow CNN for mobile environments. Additionally, we examined Convolutional and Fully Connected autoencoders for anomaly detection. Both CNN architectures achieved ~89% authentication accuracy, with the shallow CNN requiring fewer parameters. Autoencoder approaches achieved ~75% accuracy. The results demonstrate that finger-drawn symbol authentication provides a viable, secure, and user-friendly biometric solution for touchscreen devices. This approach can be integrated with existing pattern-based authentication methods to create multi-layered security systems for mobile applications.
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