TY - GEN
T1 - DeepIdentifier
T2 - 15th International Conference on Advanced Data Mining and Applications, ADMA 2019
AU - Lee, Meng Chieh
AU - Huang, Yu
AU - Ying, Josh Jia Ching
AU - Chen, Chien
AU - Tseng, Vincent Shin-Mu
PY - 2019/11/21
Y1 - 2019/11/21
N2 - 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.
AB - 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.
KW - Biometric analysis
KW - Convolutional neural networks
KW - Identity recognition
KW - Model reduction
UR - http://www.scopus.com/inward/record.url?scp=85076517442&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-35231-8_28
DO - 10.1007/978-3-030-35231-8_28
M3 - Conference contribution
AN - SCOPUS:85076517442
SN - 9783030352301
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 389
EP - 405
BT - Advanced Data Mining and Applications - 15th International Conference, ADMA 2019, Proceedings
A2 - Li, Jianxin
A2 - Wang, Sen
A2 - Qin, Shaowen
A2 - Li, Xue
A2 - Wang, Shuliang
PB - Springer
Y2 - 21 November 2019 through 23 November 2019
ER -