DeepKey: a multimodal biometric authentication system via deep decoding gaits and brainwaves

Xiang Zhang, Lina Yao, Chaoran Huang, Tao Gu, Zheng Yang, Yunhao Liu

Research output: Contribution to journalArticlepeer-review

23 Citations (Scopus)


Biometric authentication involves various technologies to identify individuals by exploiting their unique, measurable physiological and behavioral characteristics. However, traditional biometric authentication systems (e.g., face recognition, iris, retina, voice, and fingerprint) are at increasing risks of being tricked by biometric tools such as anti-surveillance masks, contact lenses, vocoder, or fingerprint films. In this article, we design a multimodal biometric authentication system named DeepKey, which uses both Electroencephalography (EEG) and gait signals to better protect against such risk. DeepKey consists of two key components: an Invalid ID Filter Model to block unauthorized subjects, and an identification model based on attention-based Recurrent Neural Network (RNN) to identify a subject's EEG IDs and gait IDs in parallel. The subject can only be granted access while all the components produce consistent affirmations to match the user's proclaimed identity. We implement DeepKey with a live deployment in our university and conduct extensive empirical experiments to study its technical feasibility in practice. DeepKey achieves the False Acceptance Rate (FAR) and the False Rejection Rate (FRR) of 0 and 1.0%, respectively. The preliminary results demonstrate that DeepKey is feasible, shows consistent superior performance compared to a set of methods, and has the potential to be applied to the authentication deployment in real-world settings.

Original languageEnglish
Article number49
Number of pages24
JournalACM Transactions on Intelligent Systems and Technology
Issue number4
Publication statusPublished - Jul 2020
Externally publishedYes


  • EEG (Electroencephalography)
  • biometric authentication
  • deep learning
  • gait
  • multimodal


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