A k-NN Classification based VR User Verification using Eye Movement and Ocular Biomechanics

VR user identification is of utmost importance especially with the increased applications of VR that will include e-payment among other applications that requires a high level of security. Biometric identification through eye movement, has been used previously due to the intrinsic characteristics of eye movement that characterises a person uniquely. In this paper, we propose using eye movement along with extraocular muscle activations in VR user verification. The muscle activations are calculated using an ocular biomechanical model. The k-NN classification results showed approximately 90% accuracy when using a feature set with eye movement parameters (3 joint angles), muscle activations for all 6 muscles along with the VR object position in 3D. The classifier is a biometric VR user verification tool that provides an easy and non-intrusive methods that can be easily integrated in different VR applications that require user verification.

[1]  Julie Iskander,et al.  Ergonomic effects of using Lift Augmentation Devices in mining activities , 2016, 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

[2]  S Nahavandi,et al.  An ocular biomechanic model for dynamic simulation of different eye movements. , 2018, Journal of biomechanics.

[3]  Florian Alt,et al.  Seamless and Secure VR: Adapting and Evaluating Established Authentication Systems for Virtual Reality , 2017 .

[4]  Saeid Nahavandi,et al.  A Review on Ocular Biomechanic Models for Assessing Visual Fatigue in Virtual Reality , 2018, IEEE Access.

[5]  Wafa Barkhoda,et al.  Rotation invariant retina identification based on the sketch of vessels using angular partitioning , 2009, 2009 International Multiconference on Computer Science and Information Technology.

[6]  Saeid Nahavandi,et al.  Simulating eye-head coordination during smooth pursuit using an ocular biomechanic model , 2017, 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

[7]  J. Franklin,et al.  The elements of statistical learning: data mining, inference and prediction , 2005 .

[8]  S. Delp,et al.  Influence of Muscle Morphometry and Moment Arms on the Moment‐Generating Capacity of Human Neck Muscles , 1998, Spine.

[9]  Ayman Habib,et al.  OpenSim: Open-Source Software to Create and Analyze Dynamic Simulations of Movement , 2007, IEEE Transactions on Biomedical Engineering.

[10]  Trevor Darrell,et al.  Nearest-Neighbor Methods in Learning and Vision , 2008, IEEE Trans. Neural Networks.

[11]  Robert Tibshirani,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.

[12]  Saeid Nahavandi,et al.  Measuring depth accuracy in RGBD cameras , 2013, 2013, 7th International Conference on Signal Processing and Communication Systems (ICSPCS).

[13]  Lalitha Rangarajan,et al.  Face Identification from Manipulated Facial Images Using SIFT , 2010, 2010 3rd International Conference on Emerging Trends in Engineering and Technology.

[14]  Cecilia R. Aragon,et al.  Biometric identification via an oculomotor plant mathematical model , 2010, ETRA.

[15]  Julie Iskander,et al.  Biomechanical Analysis of Eye Movement in Virtual Environments: A Validation Study , 2018, 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

[16]  L.M. Waghmare,et al.  An Automated Iris Image Localization in Eye Images used for Personal Identification , 2006, 2006 International Conference on Advanced Computing and Communications.

[17]  Brenda K. Wiederhold,et al.  ECG to identify individuals , 2005, Pattern Recognit..

[18]  Saeid Nahavandi,et al.  Real Time Ergonomic Assessment for Assembly Operations Using Kinect , 2013, 2013 UKSim 15th International Conference on Computer Modelling and Simulation.

[19]  Saeid Nahavandi,et al.  Human Identification From ECG Signals Via Sparse Representation of Local Segments , 2013, IEEE Signal Processing Letters.

[20]  Rong Wang,et al.  Fingerprint Identification , 2008, Wiley Encyclopedia of Computer Science and Engineering.

[21]  Jeffrey A Reinbolt,et al.  OpenSim: a musculoskeletal modeling and simulation framework for in silico investigations and exchange. , 2011, Procedia IUTAM.

[22]  F. Gianfelici,et al.  Nearest-Neighbor Methods in Learning and Vision (Shakhnarovich, G. et al., Eds.; 2006) [Book review] , 2008 .

[23]  A. El Saddik,et al.  Characterizing biometric behavior through haptics and Virtual Reality , 2008, 2008 42nd Annual IEEE International Carnahan Conference on Security Technology.

[24]  Zheru Chi,et al.  Video-based biometric identification using eye tracking technique , 2012, 2012 IEEE International Conference on Signal Processing, Communication and Computing (ICSPCC 2012).

[25]  Saeid Nahavandi,et al.  Using biomechanics to investigate the effect of VR on eye vergence system. , 2019, Applied ergonomics.