自适配权重特征融合的持续身份认证

针对现有智能手机用户身份认证方法的不足，提出了一种自适配权重特征融合的持续身份认证方法。设计了一种卷积神经网络，对手机内置传感器(加速度计、陀螺仪、磁力计)获取的用户行为信息数据进行深度特征提取及融合。通过网络中3个子网络流分别提取3种传感器特征，在特征融合层加权融合，各特征的权值会在网络学习过程中根据不同特征的贡献度实现自适应分配。融合特征经过特征选择之后，使用单分类支持向量机进行用户分类认证。实验结果表明：该方法对不同用户身份认证获得的等错误率为1.20%,与现有其他认证方法相比具有更好的认证准确性。

Continuous authentication based on adaptive deep feature fusion

To address the shortcomings of the existing smartphone user authentication methods, this paper proposes a continuous identity authentication method based on adaptive weight feature fusion. A convolution neural network is designed to extract and fuse the deep features of user behavior information data obtained from the built-in sensors (accelerometer, gyroscope, magnetometer) of mobile phones. In the network, three kinds of sensor features are extracted from the three sub-network flows respectively, and weighted fusion is performed in the feature fusion layer. The weight of each feature is adaptively assigned according to the contribution of different features in the network learning process. After feature selection for fused features, one-class support vector machine is used for user classification and authentication. The experimental results show that this method achieves an equal error rate of 1.20% for different user authentication. Compared with other existing authentication methods, the proposed method demonstrates better authentication accuracy.