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Biometric authentication involves various technologies to identifyindividuals by exploiting their unique, measurable physiological and behavioralcharacteristics. However, traditional biometric authentication systems (e.g.,face recognition, iris, retina, voice, and fingerprint) are facing anincreasing risk of being tricked by biometric tools such as anti-surveillancemasks, contact lenses, vocoder, or fingerprint films. In this paper, we designa multimodal biometric authentication system named Deepkey, which uses bothElectroencephalography (EEG) and gait signals to better protect against suchrisk. Deepkey consists of two key components: an Invalid ID Filter Model toblock unauthorized subjects and an identification model based onattention-based Recurrent Neural Network (RNN) to identify a subject`s EEG IDsand gait IDs in parallel. The subject can only be granted access while all thecomponents produce consistent evidence to match the user`s proclaimed identity.We implement Deepkey with a live deployment in our university and conductextensive 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 thatDeepkey is feasible, show consistent superior performance compared to a set ofmethods, and has the potential to be applied to the authentication deploymentin real world settings.

DeepKey: An EEG and Gait Based Dual-Authentication System