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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


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.

Original languageEnglish
Title of host publicationAdvanced Data Mining and Applications - 15th International Conference, ADMA 2019, Proceedings
EditorsJianxin Li, Sen Wang, Shaowen Qin, Xue Li, Shuliang Wang
Number of pages17
ISBN (Print)9783030352301
StatePublished - 1 Jan 2019
Event15th International Conference on Advanced Data Mining and Applications, ADMA 2019 - Dalian, China
Duration: 21 Nov 201923 Nov 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11888 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


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


  • Biometric analysis
  • Convolutional neural networks
  • Identity recognition
  • Model reduction

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