Manifold learning for user profiling and identity verification using motion sensors: CNN-designed architecture and models
Published: 1 May 2020| Version 3 | DOI: 10.17632/mgcgv9ztyb.3
Contributors:
Geise Santos, Paulo Henrique Pisani, Roberto Leyva, Chang-Tsun Li, Tiago Tavares, Anderson RochaDescription
The table Designed CNN Architecture describes the final designed CNN model for the proposed method, which was inspired in the MobileNet architecture. The number of parameters of each layer and the total of this model are specified in this table. The table Convolution Blocks contains details about the different convolution layers adopted in the architecture. We also made available the weights of both trained models: Spectrogram model and ADT model.
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Categories
Mobile Device, Biometrics, Authentication, Accelerometer, Gait, Deep Learning, Motion Sensor