Mobile Interactive Hologram Verification
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This document presents a study on the feasibility of mobile hologram verification, focusing on user-guided alignment and mobile augmented reality (AR) prototypes for capturing hologram patches. The evaluation indicates that users could verify holograms effectively with both AR and manual methods, although the AR system required higher physical effort and displayed lower usability. Future work aims to enhance tracking robustness and lessen user strain while verifying the system against real fakes.
Mobile Interactive Hologram Verification
- 1. Graz University of Technology 1hartl@icg.tugraz.at ISMAR 2013 Mobile Interactive Hologram Verification A. Hartl, J. Grubert, D. Schmalstieg, G. Reitmayr hartl@icg.tugraz.at
- 2. Graz University of Technology 2hartl@icg.tugraz.at ISMAR 2013 Motivation View-Dependent Elements Strong dependence on viewing angle/light sources Security elements (e.g., holograms) Often only a few distinct views Color copies, substitutes etc. Document Verification ID-cards, passports, banknotes Checking security elements Several security levels => Level 1: check with a manual
- 3. Graz University of Technology 3hartl@icg.tugraz.at ISMAR 2013 Contribution Hologram Verification with Mobiles – Feasibility Check Capturing hologram patches View alignment/user guidance Mobile AR prototype Evaluation within user study Revision of the prototype
- 4. Graz University of Technology 4hartl@icg.tugraz.at ISMAR 2013 Hologram Capture Assumption Total radiance from a point on the hologram is dominated by a single light source Fixed distance to surface Dominant Light Source LED light on mobile phone Fixed to the camera with offset vector o: l ~ P + R . o Representation Spatially Varying BRDF I=I(x,y,l,d); l,d as unit vectors; 2 DOF each 6D appearance model per channel High frequency representation for capturing sharp edges required => use of texture, keeping warped patches 5D model; indexed by R and location x,y on the document
- 5. Graz University of Technology 5hartl@icg.tugraz.at ISMAR 2013 View Alignment Objective Mobile method for alignment with a given 6 DOF pose Reasonable accuracy despite mobile environment Approach Visual guidance with iron sights and a virtual horizon 1 matching direction of the viewing ray 2 position along the ray 3 in-plane rotation 1 2 3
- 6. Graz University of Technology 6hartl@icg.tugraz.at ISMAR 2013 Evaluation – User Study Questions 1. How accurate can the user acquire viewing directions with guidance? 2. Can the user verify a hologram with the proposed approach? 3. How would the approach compare with a digital manual? 4. Would it be feasible to build an automatic system? Study – Hologram Verification Task Wrapped 50 Euro banknotes; AR prototype vs. check with a manual Controlled environment Learning phase for both AR system and digital manual No hints on similarity/dissimilarity Participants could stop at any time Decision on validity required Participants 17 volunteers; 1 female Little experience with holograms
- 7. Graz University of Technology 7hartl@icg.tugraz.at ISMAR 2013 Maneuvering and Task Performance Alignment Error Translation: -8mm-10mm Rotation: -8 degrees - 8 degrees Largest error with first view Reasonable accuracy for non-experts. Task Completion Times and Soundness No difference in soundness „I think, the hologram is real.“ Significant effect of interface on completion time; higher temp. effort with AR system Users were able to verify holograms with both setups.
- 8. Graz University of Technology 8hartl@icg.tugraz.at ISMAR 2013 Subjective Assessment NASA TLX Weighted Scores Workload assessment Higher physical/temporal demand with AR system: users are forced to move to the right pose No clear evidence of either interface on validity of the element AttrakDiff Questionnaire Measuring attractiveness, usability lower usability for AR system Higher scores for hedonic dimensions
- 9. Graz University of Technology 9hartl@icg.tugraz.at ISMAR 2013 Subjective Assessment and Automation IMI (Intrinsic Motivation Inventory) Measuring intrinsic motivation No effect on value/usefulness Significant effect on interest/enjoyment: higher motivation with AR system Patch Matching Registration with optical flow Normalized cross correlation (NCC) 4/6 views have NCC scores > 0.75 Repeatable image capture of hologram patches
- 10. Graz University of Technology 10hartl@icg.tugraz.at ISMAR 2013 Revised Prototype Comments from User Study Physical strain => automatic recapture during final alignment Cognitive load => display of captured data, change of local decisions Live-view; alignment ranges Informal Study (7 Participants) Comparison with previous iteration Higher confidence (5/7, 2 equal) Equal strain/effort (5/7, 2 less) Live-view and visual cues are useful (verbal) ISMAR Demo Session recorded patch live-view reference patch
- 11. Graz University of Technology 11hartl@icg.tugraz.at ISMAR 2013 Conclusion Feasibility check for hologram verification on mobiles Mobile SVBRDF image capture using dominant light source User guidance approach using iron sights and the virtual horizon Mobile AR prototype system Evaluation in user study and comparison with digital manual Findings Capture of holograms and view alignment worked reasonably well. Higher motivation to use the AR system, but it did not provide more value. Hologram verification on mobiles seams feasible. Future work Evaluation of the revised prototype with substitutes or real fakes. Crafting a less straining approach with dense matching. Improving tracking robustness. This work is supported by Bundesdruckerei GmbH.
- 12. Graz University of Technology 12hartl@icg.tugraz.at ISMAR 2013 References R. L. van Renesse. Optical Document Security. Artech House, third edition, 2005. T.-H. Park and H.-J. Kwon. Vision inspection system for holograms with mixed patterns. In CASE, pages 563–567, 2010. M. Haindl and J. Filip. Visual Texture. Advances in Computer Vision and Pattern Recognition. Springer Verlag, 2013 J. Jachnik, R. A. Newcombe, and A. J. Davison. Real-time surface light-field capture for augmentation of planar specular surfaces. In ISMAR, pages 91–97, 2012 P. Ren, J. Wang, J. Snyder, X. Tong, and B. Guo. Pocket reflectometry. n Proc. SIGGRAPH 2011, SIGGRAPH ’11, pages 45:1–45:10, New York, NY, USA, 2011. ACM. Y.-C. Cheng, J.-Y. Lin, C.-W. Yi, Y.-C. Tseng, L.-C. Kuo, Y.-J. Yeh, and C.-W. Lin. Ar-based positioning for mobile devices. In ICPPW, pages 63– 70, 2011 K. Chintamani, A. Cao, R. Ellis, and A. Pandya. Improved telemanipulator navigation during display-control misalignments using augmented reality cues. Systems, Man and Cybernetics, Part A: Systems and Humans, 40(1):29–39, 2010 S. G. Hart and L. E. Staveland. Human Mental Workload, chapter Development of NASA-TLX (Task Load Index): Results of empirical and theoretical research. North Holland Press, Amsterdam, 1988 M. Hassenzahl, M. Burmester, and F. Koller. AttrakDiff: Ein Frage-bogen zur Messung wahrgenommener hedonischer und pragmatischer Qualität. In Mensch & Computer 2003: Interaktion in Bewegung, pages 187–196, Stuttgart, Germany, 2003. B. G. Teubner. E. McAuley, T. Duncan, and V. V. Tammen. Psychometric properties of the intrinsic motivation inventory in a competitive sport setting: A confirmatory factor analysis. Research quarterly for exercise and sport, 60(1):48–58, 1989 T. P. M., Urschler, C. Zach, R. Beichel, and H. Bischof. A duality based algorithm for tv-l1-optical-flow image registration. In MICCAI, pages 511– 518, 2007