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.

[1]  Ling Huang,et al.  Short paper: smartphones: not smart enough? , 2012, SPSM '12.

[2]  Rama Chellappa,et al.  Deep feature-based face detection on mobile devices , 2016, 2016 IEEE International Conference on Identity, Security and Behavior Analysis (ISBA).

[3]  Clayton Shepard,et al.  LiveLab: measuring wireless networks and smartphone users in the field , 2011, SIGMETRICS Perform. Evaluation Rev..

[4]  Hans-Peter Kriegel,et al.  Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and Its Applications , 1998, Data Mining and Knowledge Discovery.

[5]  Rama Chellappa,et al.  Partial face detection for continuous authentication , 2016, 2016 IEEE International Conference on Image Processing (ICIP).

[6]  Tao Feng,et al.  Continuous mobile authentication using touchscreen gestures , 2012, 2012 IEEE Conference on Technologies for Homeland Security (HST).

[7]  Deva Ramanan,et al.  Face detection, pose estimation, and landmark localization in the wild , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[8]  Rama Chellappa,et al.  A deep pyramid Deformable Part Model for face detection , 2015, 2015 IEEE 7th International Conference on Biometrics Theory, Applications and Systems (BTAS).

[9]  Antti Evesti,et al.  Context-aware systems and adaptive user authentication , 2013, AmI 2013.

[10]  Savvas Argyropoulos,et al.  Four Machine Learning Algorithms for Biometrics Fusion: A Comparative Study , 2012, Appl. Comput. Intell. Soft Comput..

[11]  Rama Chellappa,et al.  Face Alignment by Local Deep Descriptor Regression , 2016, ArXiv.

[12]  Andrew Zisserman,et al.  Detecting People Looking at Each Other in Videos , 2014, International Journal of Computer Vision.

[13]  Arun Ross,et al.  Information fusion in biometrics , 2003, Pattern Recognit. Lett..

[14]  Horst Bischof,et al.  Annotated Facial Landmarks in the Wild: A large-scale, real-world database for facial landmark localization , 2011, 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops).

[15]  Rajesh Kumar,et al.  Context-Aware Active Authentication Using Smartphone Accelerometer Measurements , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops.

[16]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[17]  Matti Pietikäinen,et al.  Bi-Modal Person Recognition on a Mobile Phone: Using Mobile Phone Data , 2012, 2012 IEEE International Conference on Multimedia and Expo Workshops.

[18]  Marwan Mattar,et al.  Labeled Faces in the Wild: A Database forStudying Face Recognition in Unconstrained Environments , 2008 .

[19]  Rama Chellappa,et al.  Touch Gesture-Based Active User Authentication Using Dictionaries , 2015, 2015 IEEE Winter Conference on Applications of Computer Vision.

[20]  Dawn Xiaodong Song,et al.  Touchalytics: On the Applicability of Touchscreen Input as a Behavioral Biometric for Continuous Authentication , 2012, IEEE Transactions on Information Forensics and Security.

[21]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[22]  Rama Chellappa,et al.  Face-based Active Authentication on mobile devices , 2015, 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[23]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[24]  Rama Chellappa,et al.  Unconstrained face verification using deep CNN features , 2015, 2016 IEEE Winter Conference on Applications of Computer Vision (WACV).

[25]  Rama Chellappa,et al.  Attribute-based continuous user authentication on mobile devices , 2015, 2015 IEEE 7th International Conference on Biometrics Theory, Applications and Systems (BTAS).

[26]  Christoph Busch,et al.  Unobtrusive User-Authentication on Mobile Phones Using Biometric Gait Recognition , 2010, 2010 Sixth International Conference on Intelligent Information Hiding and Multimedia Signal Processing.

[27]  Chuan Qin,et al.  Progressive Authentication: Deciding When to Authenticate on Mobile Phones , 2012, USENIX Security Symposium.

[28]  J. Friedman.  Greedy function approximation: A gradient boosting machine. , 2001 .

[29]  Sébastien Gambs,et al.  Show me how you move and I will tell you who you are , 2010, SPRINGL '10.

[30]  Marc-Olivier Killijian,et al.  Next place prediction using mobility Markov chains , 2012, MPM '12.

[31]  Anil K. Jain,et al.  Continuous authentication of mobile user: Fusion of face image and inertial Measurement Unit data , 2015, 2015 International Conference on Biometrics (ICB).

[32]  Vyas Sekar,et al.  Measuring user confidence in smartphone security and privacy , 2012, SOUPS.

[33]  Steven P. Weber,et al.  Active Authentication on Mobile Devices via Stylometry, Application Usage, Web Browsing, and GPS Location , 2017, IEEE Systems Journal.

[34]  Lee Luan Ling,et al.  User authentication through typing biometrics features , 2005, IEEE Transactions on Signal Processing.

[35]  Alex Fridman,et al.  Learning Human Identity from Motion Patterns , 2015, IEEE Access.

[36]  Vir V. Phoha,et al.  Which verifiers work?: A benchmark evaluation of touch-based authentication algorithms , 2013, 2013 IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS).

[37]  Matti Pietikäinen,et al.  Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[38]  Rama Chellappa,et al.  Continuous User Authentication on Mobile Devices: Recent progress and remaining challenges , 2016, IEEE Signal Processing Magazine.

[39]  PentlandAlex,et al.  Reality mining: sensing complex social systems , 2006 .

[40]  Shengcai Liao,et al.  Learning Face Representation from Scratch , 2014, ArXiv.

[41]  Alex Pentland,et al.  Reality mining: sensing complex social systems , 2006, Personal and Ubiquitous Computing.