Research Article
A Lightweight Deep Learning Algorithm for Identity Recognition
@INPROCEEDINGS{10.1007/978-3-030-64002-6_1, author={Yanjie Cao and Zhiyi Zhou and Pengsong Duan and Chao Wang and Xianfu Chen}, title={A Lightweight Deep Learning Algorithm for Identity Recognition}, proceedings={Mobile Networks and Management. 10th EAI International Conference, MONAMI 2020, Chiba, Japan, November 10--12, 2020, Proceedings}, proceedings_a={MONAMI}, year={2020}, month={12}, keywords={Identity recognition WiFi Channel state information Deep learning}, doi={10.1007/978-3-030-64002-6_1} }- Yanjie Cao
Zhiyi Zhou
Pengsong Duan
Chao Wang
Xianfu Chen
Year: 2020
A Lightweight Deep Learning Algorithm for Identity Recognition
MONAMI
Springer
DOI: 10.1007/978-3-030-64002-6_1
Abstract
The challenges in current WiFi based gait recognition models, such as the limited classification ability, high storage cost, long training time and restricted deployment on hardware platforms, motivate us to propose a lightweight gait recognition system, which is named as B-Net. By reconstructing original data into a frequency energy graph, B-Net extracts the spatial features of different carriers. Moreover, a Balloon mechanism based on the concept of channel information integration is designed to reduce the storage cost, training time and so on. The key benefit of the Balloon mechanism is to realize the compression of model scale and relieve the gradient disappearance to some extent. Experimental results show that B-Net has less parameters and training time and is with higher accuracy and better robustness, compared with the previous gait recognition models.
