Continuous face authentication scheme for mobile devices with tracking and liveness detection

Abstract

We present a novel scheme for continuous face authentication using mobile device cameras that addresses the issue of spoof attacks and attack windows in state-of-the-art approaches. Our scheme authenticates a user based on extracted facial features. However, unlike other schemes that periodically re-authenticate a user, our scheme tracks the authenticated face and only attempts re-authentication when the authenticated face is lost. This allows our scheme to eliminate attack windows that exist in schemes authenticating periodically and immediately recognise impostor usage. We also introduce a robust liveness detection component to our scheme that can detect printed faces and face videos. We describe how the addition of liveness detection enhances the robustness of our scheme against spoof attacks, improving on stateof-the-art approaches that lack this capability. Furthermore, we create the first dataset of facial videos collected from mobile devices during different real-world activities (walking, sitting and standing) such that our results reflect realistic scenarios. Our dataset therefore allows us to give new insight into the impact of user activity on facial recognition. Our dataset also includes spoofed facial videos for liveness testing. We use our dataset alongside two benchmark datasets for our experiments. We show and discuss how our scheme improves on existing continuous face authentication approaches and efficiently enhances device security.

Dense Deep Neural Network Architecture for Keystroke Dynamics Authentication in Mobile Phone

“…The SVM-HMM model provides the best result when compared with naïve Bayes and cosine similarity. In [22], the author trained SVM classifier on facial attributes extracted from real images to build a continuous classification scheme. Results indicated that the proposed scheme shows a significant improvement on the existing continuous facial recognition system.…”

Multifactor Authentication Protocol in a Mobile Environment

“…Correct classification can be achieved by using classifiers, such as SVM (Support Vector Machine), DT (Decision Tree), and K-NN (k nearest neighbors). Online authentication is also possible in combination with physiological biometric authentication [12]. For example, the characteristic features of the face obtained with a smartphone camera can be used as a source of reference data.…”

“…Several of the thus far presented authentication schemes [7], [9], [10], [12] designed for the mobile environment focus on a single authentication factor. Thus, these protocols are not in line with the strong authentication objectives of EU Regulations [1] and NIST requirements [2] [3].…”

A Mechanism of Masking Identification Information regarding Moving Objects Recorded on Visual Surveillance Systems by Differentially Implementing Access Permission

“…As the masking module performs masking on objects that have been recognized by the object identification module, the access permission DB module holds authentication information that discerns the status of the access permission granted to the user on each object (allowed vs. denied) and offers authentication information regarding each object upon requests from the masking module. Depending on the level of access rights stored in the access permission DB module, requesters of visual information may obtain different results from the same visual information due to the difference in the scope of masking provided [62][63][64][65].…”

“…visual information may obtain different results from the same visual information due to the difference in the scope of masking provided [62][63][64][65].…”