Unobtrusive User-Authentication on Mobile Phones Using Biometric Gait Recognition

By Mohammad Omar Derawi, Claudia Nickel, Patrick A. H. Bours, Christoph Busch. 2010 Sixth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, 2010

394 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/b500e77ec059241c81f8dcaaa7d6ff126ff81854

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In Biomedical Engineering

In Computer Vision and Pattern Recognition

In Electrical and Electronic Engineering

Citations in the corpus

You Are How You Touch: User Verification on Smartphones via Tapping Behaviors (2014, background): “1%) [32], [33], gesture-based scheme [34], or the unique movement users perform when answering or placing a phone call (EER being between 4.”
HMOG: New Behavioral Biometric Features for Continuous Authentication of Smartphone Users (2015, positive): “Continuous or active authentication addresses these challenges by frequently and unobtrusively authenticating the user via behavioral biometric signals, such as touchscreen interactions [26], hand movements and gait [17], [8], voice [40], and phone location [55].”
Cancelable Biometrics: A review (2015, background): “Examples include systems based on screen touch gestures [63], gait recognition [64], and device movement patterns (as measured by the accelerometer) [65].”
Learning Human Identity From Motion Patterns (2015, neutral): “Derawi et al. [9], for example, used a smartphone attached to the human body to extract information about walking cycles, achieving 20 .”
Touchalytics: On the Applicability of Touchscreen Input as a Behavioral Biometric for Continuous Authentication (2012, background): “From a usability perspective, traditional authentication schemes are inconvenient because users must focus on the authentication step every time they begin interacting with their device.”
A review of biometric technology along with trends and prospects (2014, background): “Several approaches have been developed so far thereby the authors claimed to achieve better recognition accuracy [199, 200, 201, 202, 203].”
Inertial Sensor-Based Gait Recognition: A Review (2015, methodology): “Existing cycle-based gait recognition approaches rely on gait detection methods that leverage local extrema analysis in a most common way [23,24,31,32,49,60–67], as well as zero-crossing detection [25,40,41,58,68], salience vector detection [69,70], phase analysis [28,42], detection of dynamic…”
Continuous User Authentication on Mobile Devices: Recent progress and remaining challenges (2016, methodology): “In recent years, a number of different methods have been developed for gait-based recognition on mobile devices [34], [35], [36], [37], [38], [39], [40].”