Seamless and Secure VR: Adapting and Evaluating Established Authentication Systems for Virtual Reality

Virtual reality (VR) headsets are enabling a wide range of new opportunities for the user. For example, in the near future users may be able to visit virtual shopping malls and virtually join international conferences. These and many other scenarios pose new questions with regards to privacy and security, in particular authentication of users within the virtual environment. As a first step towards seamless VR authentication, this paper investigates the direct transfer of well-established concepts (PIN, Android unlock patterns) into VR. In a pilot study (N = 5) and a lab study (N = 25), we adapted existing mechanisms and evaluated their usability and security for VR. The results indicate that both PINs and patterns are well suited for authentication in VR. We found that the usability of both methods matched the performance known from the physical world. In addition, the private visual channel makes authentication harder to observe, indicating that authentication in VR using traditional concepts already achieves a good balance in the trade-off between usability and security. The paper contributes to a better understanding of authentication within VR environments, by providing the first investigation of established authentication methods within VR, and presents the base layer for the design of future authentication schemes, which are used in VR environments only

Recurrent Attention Models for Depth-Based Person Identification

We present an attention-based model that reasons on human body shape and motion dynamics to identify individuals in the absence of RGB information, hence in the dark. Our approach leverages unique 4D spatio-temporal signatures to address the identification problem across days. Formulated as a reinforcement learning task, our model is based on a combination of convolutional and recurrent neural networks with the goal of identifying small, discriminative regions indicative of human identity. We demonstrate that our model produces state-of-the-art results on several published datasets given only depth images. We further study the robustness of our model towards viewpoint, appearance, and volumetric changes. Finally, we share insights gleaned from interpretable 2D, 3D, and 4D visualizations of our model's spatio-temporal attention.Comment: Computer Vision and Pattern Recognition (CVPR) 201

Infrared face recognition: a comprehensive review of methodologies and databases

Automatic face recognition is an area with immense practical potential which includes a wide range of commercial and law enforcement applications. Hence it is unsurprising that it continues to be one of the most active research areas of computer vision. Even after over three decades of intense research, the state-of-the-art in face recognition continues to improve, benefitting from advances in a range of different research fields such as image processing, pattern recognition, computer graphics, and physiology. Systems based on visible spectrum images, the most researched face recognition modality, have reached a significant level of maturity with some practical success. However, they continue to face challenges in the presence of illumination, pose and expression changes, as well as facial disguises, all of which can significantly decrease recognition accuracy. Amongst various approaches which have been proposed in an attempt to overcome these limitations, the use of infrared (IR) imaging has emerged as a particularly promising research direction. This paper presents a comprehensive and timely review of the literature on this subject. Our key contributions are: (i) a summary of the inherent properties of infrared imaging which makes this modality promising in the context of face recognition, (ii) a systematic review of the most influential approaches, with a focus on emerging common trends as well as key differences between alternative methodologies, (iii) a description of the main databases of infrared facial images available to the researcher, and lastly (iv) a discussion of the most promising avenues for future research.Comment: Pattern Recognition, 2014. arXiv admin note: substantial text overlap with arXiv:1306.160

Efficient Verifiable Computation of XOR for Biometric Authentication

This work addresses the security and privacy issues in remotebiometric authentication by proposing an efficient mechanism to verifythe correctness of the outsourced computation in such protocols.In particular, we propose an efficient verifiable computation of XORingencrypted messages using an XOR linear message authenticationcode (MAC) and we employ the proposed scheme to build a biometricauthentication protocol. The proposed authentication protocol is bothsecure and privacy-preserving against malicious (as opposed to honest-but-curious) adversaries. Specifically, the use of the verifiable computation scheme together with an homomorphic encryption protects the privacy of biometric templates against malicious adversaries. Furthermore, in order to achieve unlinkability of authentication attempts, while keeping a low communication overhead, we show how to apply Oblivious RAM and biohashing to our protocol. We also provide a proof of security for the proposed solution. Our simulation results show that the proposed authentication protocol is efficient