Eye Movement Biometrics: Its Future and Applications. Dr. Oleg Komogortsev, Texas State University

Transcription

1 Eye Movement Biometrics: Its Future and Applications Dr. Oleg Komogortsev, Texas State University

2 Eye Tracking Technology (x,y,t)

3 Working Principle Hardware IR light Image sensor Eye Features Corneal reflection Pupil

4 Eye Tracker vs. Iris Scanner Eye Tracker Iris Scanner Eye tribe eye tracker CMITech DMX-10 Iris Scanner

5 Important Eye Tracker Characteristics Sampling rate 15-20Hz Spatial accuracy Spatial precision >0.02

6 Grand Vision: Ocular Biometrics Oculomotor Plant Brain Ocular Biometrics Goals: Higher security Better accuracy Iris

7 HVS: eye movement types Eye-fixations high acuity vision eye is stable in regard to the object of interest eye speed < 5 /sec (or < 30 /sec ) Saccades no vision fastest movement in human move eyes between eye-fixations very rapidly eye speed up to 700 /sec 1 2

8 Where & How by Two Users

9 Eye Movement Control Brain Oculomotor plant extraocular muscles + eye globe with surrounding tissues neuronal control signal WHERE HOW HOW

10 Oculomotor Plant Characteristics Passive elasticity Neuronal control signal Active state tension Length tension relationship Force velocity relationship Series elasticity Eye globe inertia Viscous properties of the eye globe

11 Muscle Immobile State - Forces Muscle Lateral Rectus 0 F LR N LR N LR - muscle innervation through motoneuronal firing by Central Nervous System. F LR active force is generated to contract the muscle T LR K SE θ SE_LR θ SE_LR B θ LT_LR K LT θ LT_LR θ LT_LR K LT θ LT_LR additional contractile force is added through springlike length-tension component K SE θ SE_LR spring-like serieselasticity component resists contractile force θ LR

12 Agonist Forces Muscle lateral rectus. Role agonist. Action contraction. N LR 0 + K SE (θ SE_LR + θ SE_LR ) F LR B AG LT _ LR B AG V damping (force velocity component) resists contraction when a muscle shortens T LR K LT (θ LT_LR - θ LT_LR ) θ SE_LR + θ SE_LR θ LT_LR - θ LT_LR K SE (Dq LT _ LR - Dq) = - DqK SE K LT K SE + K LT + K SE ˆF LR K SE + K LT - ˆB AG Dq LT _ LR θ LR - θ

13 2D Oculomotor Plant Model 12 Differential Equations 36 OP Characteristics The model simulates saccades Oculomotor Plant Ocular Biometrics

14 Biometrics via Complex Eye Movement Patterns Image courtesy of

15 CEM Characteristics Fixation related Number of fixations Fixation duration

16 CEM Characteristics Saccade related Saccade amplitude

17 CEM Characteristics Scanpath related Length Inflections

18 CEM Characteristics Scanpath related Area

19 CEM Characteristics Scanpath related Fixation distances

20 Database Characteristics 320 subjects 170 male/150 female, ages types of visual stimuli High-grade commercial eye-tracker ($30,000) EyeLink 1000, Monocular mode, captured eye: left Sampling rate: 1000 Hz Spatial accuracy: ~0.5 Spatial resolution: ~0.02 RMS Time interval ~20 minute between samples

21 Current Best Results Current Best Results * Rank-1 IR 88.6% EER 5.8% GAR at 0.1% FAR 76.7%. Past Results (CEM-B 2.5 years ago) Rank-1 IR 49.5% EER 15.4% C. Holland, and O. V. Komogortsev, Towards a multi-source fusion approach for eye movement- -, In Proceedings of the IEEE/IARP International Conference on Biometrics (ICB), 2013, pp. 1-8.

22 Bio Eye 2015 Competition: Results Rank Participant Rank-1 identification rate % RAN_Short RAN_Long TEX_Short TEX_Long FINA - Baseline* A. George and Prof. A. Routray Indian Inst. of Tech. Kharagpur N. Abe Fujitsu Laboratories Ltd. P. Kasprowski Silesian Un. of Technology Thomas Kübler Un. Tuebingen B. Galmar Independ. Research., Taiwan R. Bixler Un. of Notre Dame A. Kuehlkamp Un. of Notre Dame * C. Holland, and O. V. Komogortsev, Complex Eye Movement Pattern Biometrics: Analyzing Fixations and Saccades, In Proceedings of the IEEE/IARP International Conference on Biometrics (ICB), 2013, pp

23 Limitations: Today s Technology Requires high quality signal Calibration Cooperative subject Minimum data acquisition sample sec.

24 Applications of Eye Movement Biometrics

25 Wearables: Google Glass Google Faculty Research Award, Eye Movement-Driven Biometrics and Interaction on Google Glass, award #2014_R1_308

26 Google Glass: Colored Lens captured color

27 Google Glass: Patterned Contact Lens Demo

28 eye position (deg) Google Glass: Example of Captured Signal eye positional samples (count)

29 Google Glass: Mean Absolute Error I. Rigas and O. V. Komogortsev, Single-Pixel Eye Tracking via Patterned Contact Lenses: Design and Evaluation in HCI Domain, In Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI), 2015, pp. 1-6.

30 Liveness Detection: Iris Print Attacks 200 recorded subjects 1 professional attacker impersonating subjects Average Classification Rate (ACR) 96.5% ACR of ~90% with sampling frequency of 15Hz I. Rigas and O.V. Komogortsev, Eye movement-driven defense against iris print-attacks, Elsevier Pattern Recognition Letters, in press, 2015.

31 Liveness Detection: Attack of Mechanical Replicas 32 recorded subjects 3 mathematical models Correct Classification Rate (CR) % O. V. Komogortsev, A. Karpov, C. Holland, Attack of Mechanical Replicas: Liveness Detection With Eye Movements, IEEE Transactions on Information Forensics and Security, 10 (4), pp , 2015.

32 Detection of Health States

33 Concussion Related Metrics

34 Concussion Detection: Results 32 Subjects 30 Healthy 2 mtbi (concussed) 8 recordings per subject Detection accuracy rate 92% O. V. Komogortsev and C. Holland, The Application of Eye Movement Biometrics in the Automated Detection of Mild Traumatic Brain Injury, In Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI), 2014, pp. 1-6.

35 Conclusions Eye movement-driven biometric techniques provide Previously unexplored sources of information about a human identity Strong liveness detection capabilities Health state detection capabilities Can be run on inexpensive web-cameras and potentially on the existing iris recognition devices

36 Sponsors