Continuous 3D Face Authentication Using RGB-D Cameras

We present a continuous 3D face authentication system that uses a RGB-D camera to monitor the accessing user and ensure that only the allowed user uses a protected system. At the best of our knowledge, this is the first system that uses 3D face images to accomplish such objective. By using depth images, we reduce the amount of user cooperation that is required by the previous continuous authentication works in the literature. We evaluated our system on four 40 minutes long videos with variations in facial expressions, occlusions and pose, and an equal error rate of 0.8% was achieved.

[1]  Claudia Picardi,et al.  Keystroke analysis of free text , 2005, TSEC.

[2]  Dimitrios Tzovaras,et al.  Unobtrusive Multimodal Biometric Authentication: The HUMABIO Project Concept , 2008, EURASIP J. Adv. Signal Process..

[3]  Patrick J. Flynn,et al.  Multiple Nose Region Matching for 3D Face Recognition under Varying Facial Expression , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Anil K. Jain,et al.  Soft Biometric Traits for Continuous User Authentication , 2010, IEEE Transactions on Information Forensics and Security.

[5]  Sung-Hyuk Cha,et al.  Developing a Keystroke Biometric System for Continual Authentication of Computer Users , 2012, 2012 European Intelligence and Security Informatics Conference.

[6]  Paul J. Besl,et al.  A Method for Registration of 3-D Shapes , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Patrick J. Flynn,et al.  Overview of the face recognition grand challenge , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[8]  M. Turk,et al.  Temporal Integration for Continuous Multimodal Biometrics , 2003 .

[9]  Sandeep Kumar,et al.  Continuous Verification Using Multimodal Biometrics , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Sandeep Kumar,et al.  Using Continuous Face Verification to Improve Desktop Security , 2005, 2005 Seventh IEEE Workshops on Applications of Computer Vision (WACV/MOTION'05) - Volume 1.

[11]  Dimitrios Hatzinakos,et al.  ECG biometric analysis in cardiac irregularity conditions , 2009, Signal Image Video Process..

[12]  Juho Kannala,et al.  Joint Depth and Color Camera Calibration with Distortion Correction , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Kazimierz Kowalski,et al.  Continuous Biometric User Authentication in Online Examinations , 2010, 2010 Seventh International Conference on Information Technology: New Generations.

[14]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[15]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[16]  Patrick J. Flynn,et al.  A survey of approaches and challenges in 3D and multi-modal 3D + 2D face recognition , 2006, Comput. Vis. Image Underst..

[17]  Paul A. Viola,et al.  Robust Real-Time Face Detection , 2001, International Journal of Computer Vision.

[18]  John J. Leggett,et al.  Dynamic Identity Verification via Keystroke Characteristics , 1991, Int. J. Man Mach. Stud..

[19]  Maurício Pamplona Segundo,et al.  Real-time scale-invariant face detection on range images , 2011, 2011 IEEE International Conference on Systems, Man, and Cybernetics.

[20]  Sharath Pankanti,et al.  Guide to Biometrics , 2003, Springer Professional Computing.

[21]  Azriel Rosenfeld,et al.  Face recognition: A literature survey , 2003, CSUR.