Active User Authentication for Smartphones: A Challenge Data Set and Benchmark Results

In this paper, automated user verification techniques for smartphones are investigated. A unique non-commercial dataset, the University of Maryland Active Authentication Dataset 02 (UMDAA-02) for multi-modal user authentication research is introduced. This paper focuses on three sensors - front camera, touch sensor and location service while providing a general description for other modalities. Benchmark results for face detection, face verification, touch-based user identification and location-based next-place prediction are presented, which indicate that more robust methods fine-tuned to the mobile platform are needed to achieve satisfactory verification accuracy. The dataset will be made available to the research community for promoting additional research.Comment: 8 pages, 12 figures, 6 tables. Best poster award at BTAS 201

Human Gait Database for Normal Walk Collected by Smart Phone Accelerometer

The goal of this study is to introduce a comprehensive gait database of 93 human subjects who walked between two endpoints during two different sessions and record their gait data using two smartphones, one was attached to the right thigh and another one on the left side of the waist. This data is collected with the intention to be utilized by a deep learning-based method which requires enough time points. The metadata including age, gender, smoking, daily exercise time, height, and weight of an individual is recorded. this data set is publicly available

ViotSOC: Controlling Access to Dynamically Virtualized IoT Services using Service Object Capability

Virtualization of Internet of Things(IoT) is a concept of dynamically building customized high-level IoT services which rely on the real time data streams from low-level physical IoT sensors. Security in IoT virtualization is challenging, because with the growing number of available (building block) services, the number of personalizable virtual services grows exponentially. This paper proposes Service Object Capability(SOC) ticket system, a decentralized access control mechanism between servers and clients to effi- ciently authenticate and authorize each other without using public key cryptography. SOC supports decentralized partial delegation of capabilities specified in each server/- client ticket. Unlike PKI certificates, SOC’s authentication time and handshake packet overhead stays constant regardless of each capability’s delegation hop distance from the root delegator. The paper compares SOC’s security bene- fits with Kerberos and the experimental results show SOC’s authentication incurs significantly less time packet overhead compared against those from other mechanisms based on RSA-PKI and ECC-PKI algorithms. SOC is as secure as, and more efficient and suitable for IoT environments, than existing PKIs and Kerberos

Fireground location understanding by semantic linking of visual objects and building information models

This paper presents an outline for improved localization and situational awareness in fire emergency situations based on semantic technology and computer vision techniques. The novelty of our methodology lies in the semantic linking of video object recognition results from visual and thermal cameras with Building Information Models (BIM). The current limitations and possibilities of certain building information streams in the context of fire safety or fire incident management are addressed in this paper. Furthermore, our data management tools match higher-level semantic metadata descriptors of BIM and deep-learning based visual object recognition and classification networks. Based on these matches, estimations can be generated of camera, objects and event positions in the BIM model, transforming it from a static source of information into a rich, dynamic data provider. Previous work has already investigated the possibilities to link BIM and low-cost point sensors for fireground understanding, but these approaches did not take into account the benefits of video analysis and recent developments in semantics and feature learning research. Finally, the strengths of the proposed approach compared to the state-of-the-art is its (semi -)automatic workflow, generic and modular setup and multi-modal strategy, which allows to automatically create situational awareness, to improve localization and to facilitate the overall fire understanding