colours and faces tzu-pei grace chen sidney fels human communication technologies lab
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![Page 1: Colours and Faces Tzu-Pei Grace Chen Sidney Fels Human Communication Technologies Lab](https://reader036.vdocuments.us/reader036/viewer/2022062518/56649d225503460f949f88c1/html5/thumbnails/1.jpg)
Colours and Faces
Tzu-Pei Grace ChenSidney Fels
Human Communication Technologies Lab
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Presentation outline• Colours and face relations
• offer a good starting point to tackle face metric problem
• Results from second pilot tests
• Discussion
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Role of the human face
Identity
Communication
Attractiveness
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Challenge in face recognition
Is there a face metric system that can adequately quantify all
existing faces?
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Why is it difficult in quantifying faces?
• Faces are transient
• We have sharp face recognition skills
• Infinite dimensions and acute recognition makes it hard
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Previous work
• Face similarity metric– Eigenface [Turk and
Pentland]– Shape free face [Craw
et al and Bruce et al]
• Face attractiveness metric– Beauty mask
[Marquardt]
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Why is colour metaphor a good starting point?
• Multi-dimensional
• Well-researched (many systems)
• Has less dimension than faces
• Good to model from a smaller example
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Colour and face relations
• Colour-blindness vs. face-blindness
• Verbal over-shadowing effect
• Colour, emotion and facial expression.
• Colour vs. face opponent mechanism
• Primary colours and existence of primary faces
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Colour blindness and face blindness
Normal Blind
Colour
Face
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Opponent mechanism
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Verbal foreshadowing
• Memory of both face and colours can be impaired if verbalized after studied
• For faces, this is due to a lack of words to describe the holistic properties
• Verbal descriptions limited to face features
• Perceptual ability surpass verbal ability• Same for colours
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Plutchik’s model of emotions
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Primary faces?
• DNA evidence• Localization of
mating habit
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Second experiment
• Investigating two types of axes and two kinds of interface.
interface
axes
wheel
dynamic slider
T-S uncorrelated T-S correlated
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Findings from second pilot test
• Subjects refine their match around the range of distance [1,2] from the target
subject 5 replicate 2 correlated wheel
02468
101214
0 1
1.41
42136
1.73
20508 2
2.23
6068
2.64
57513
2.82
84271
distance to target
nu
mb
er o
f ar
riva
ls
• The hump occurs most frequently with correlated sliders
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Conclusion from pilot test
• For a small face space…
interface
axes
wheel
dynamic slider
T-S uncorrelated T-S correlated
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Summary
•Colour metaphor seems like a good starting point to tackle face metric problem•Second pilot test- work in progress
–investigates additive and subtractive face system
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The End