DeepIdentifier: A Deep Learning-Based Lightweight Approach for User Identity Recognition

Meng Chieh Lee, Yu Huang, Josh Jia Ching Ying, Chien Chen, Vincent S. Tseng*

*Corresponding author for this work

研究成果: Conference contribution同行評審


Identifying a user precisely through mobile-device-based sensing information is a challenging and practical issue as it is usually affected by context and human-action interference. We propose a novel deep learning-based lightweight approach called DeepIdentifier. More specifically, we design a powerful and efficient block, namely funnel block, as the core components of our approach, and further adopt depthwise separable convolutions to reduce the model computational overhead. Moreover, a multi-task learning approach is utilized on DeepIdentifier, which learns to recognize the identity and reconstruct the signal of the input sensor data simultaneously during the training phase. The experimental results on two real-world datasets demonstrate that our proposed approach significantly outperforms other existing approaches in terms of efficiency and effectiveness, showing up to 17 times and 40 times improvement over state-of-the-art approaches in terms of model size reduction and computational cost respectively, while offering even higher accuracy. To the best of our knowledge, DeepIdentifier is the first lightweight deep learning approach for solving the identity recognition problem. The dataset we gathered, together with the implemented source code, is public to facilitate the research community.

主出版物標題Advanced Data Mining and Applications - 15th International Conference, ADMA 2019, Proceedings
編輯Jianxin Li, Sen Wang, Shaowen Qin, Xue Li, Shuliang Wang
出版狀態Published - 1 一月 2019
事件15th International Conference on Advanced Data Mining and Applications, ADMA 2019 - Dalian, China
持續時間: 21 十一月 201923 十一月 2019


名字Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
11888 LNAI


Conference15th International Conference on Advanced Data Mining and Applications, ADMA 2019

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