mobile user interfaces for efficient verification of holograms

Teaser Image + Title

Mobile User Interfaces for Efficient Verification of Holograms

Andreas Hartl, Jens Grubert, Christian Reinbacher, Clemens Arth

and Dieter Schmalstieg Graz University of Technology

How to identify fake money?

Hologram Verification Workflow

Pre-processing

1. Record reference images and associated camera poses under controlled lighting

Online

1. Capture relevant views

2. Compare reference view with current view (automatic or manual)

Capture single views [1]: guide users to individual

pre-selected views

Capturing Relevant Views

Capture many views [2]

[1] Hartl, A., Grubert, J., Schmalstieg, D., Reitmayr, G.: Mobile interactive hologram verifcation. ISMAR, pages, 2013 [2] T.-H. Park and H.-J. Kwon. Vision inspection system for holograms with mixed patterns. CASE, pages 563–567, 2010

Capture single views [1]: naive impl. too slow

high workload

Capturing Relevant Views

Capture many views [1]: stationary equipment or

too time consuming

[1] Hartl, A., Grubert, J., Schmalstieg, D., Reitmayr, G.: Mobile interactive hologram verifcation. ISMAR, pages, 2013 [2] T.-H. Park and H.-J. Kwon. Vision inspection system for holograms with mixed patterns. CASE, pages 563–567, 2010

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Alignment (ALI)

Constrained Navigation (CON)

Hybrid Approach (HYB)

Summary View

User Evaluation • 3x4 within-subjects desing

• 19 participants

• IV: – 3 UIs

– 4 holograms (2 real, 2 fake)

• DV: – Task completion time

– System + user decision perf.

– Workload (TLX)

– Usability (ASQ)

– UX (AttrakDiff, IMI)

Findings: Task Completion Time

0

10

20

30

40

50

60

70

80

90

Interface

Tim

e (

s)

HYB CON ALI

*

Findings: Performance + UX

Performance

Correct: User (~80%), System (~73%)

Including Neutral: User (~92%), System (~84%)

UX

Nasa TLX: no sign. differences (overall: ~40/100)

ASQ (ease-of-use, task duration), AttrakDiff (Pragmatic and Hedonic qualities), Intrinsic Motivation no sig. differences

User preference: CON (47%), ALI (26%), HYB (26%)

Summary

• New UIs for sampling regions rather than precisely aligning 6 DOF poses can speed up the verification process

• still too slow for most real-world applications

• Users did not prefer the fastest UI

• Automatic matching performance poor better similarity metric