研究目的
Investigating the capability of temporal deep neural networks to interpret natural human kinematics for active biometric authentication with mobile inertial sensors.
研究成果
The study demonstrates that human kinematics can convey important information about user identity, serving as a valuable component of multi-modal authentication systems. The proposed model also shows promise for application in visual contexts.
研究不足
The study is limited by the quality and representativeness of the passively collected data, the computational constraints of mobile devices, and the need for further validation in real-world scenarios.
1：Experimental Design and Method Selection:
The study compares several neural architectures for efficient learning of temporal multi-modal data representations, including an optimized shift-invariant dense convolutional mechanism.
2：Sample Selection and Data Sources:
A first-of-its-kind dataset of human movements was passively collected by 1500 volunteers using their smartphones daily over several months.
3：List of Experimental Equipment and Materials:
Smartphones with inertial sensors (accelerometer and gyroscope) were used for data collection.
4：Experimental Procedures and Operational Workflow:
The methodology involves training neural networks on the collected dataset, incorporating dynamic features in a probabilistic generative framework, and evaluating the models for authentication accuracy.
5：Data Analysis Methods:
The performance of the models was evaluated based on their ability to authenticate users using the collected motion data.
独家科研数据包，助您复现前沿成果，加速创新突破
获取完整内容
