Date post: | 18-Jul-2015 |
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Devices & Hardware |
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Automated Prediction of
Preferences Using Facial
Expressions
David Masip, Michael S. North, Alexander Todorov, Daniel Osherson, PLOSOne
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Automated Prediction of Preferences Using Facial Expressions
Introduction: Reading minds
Detailed problem: subtle facial expressions
• State-of-the-art
• Example
Methods
• Target phase
• Perceiver phase
• Algorithm
Results
• Hypothesis one: simple classifier
• Hypothesis two: machine learning classifier
Conclusions
Índex
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Automated Prediction of Preferences Using Facial Expressions
1. Reading minds
Can we read the mind? The answer is NO. Not completely at least…
Our face reflects somehow our feelings, emotions, … We propose to read/predict
what people thinks about two stimuli in a very particular setting: Preferences
We explore whether computers can read and infer what humans believe in a binary
choosing scenario, and we use only subtle facial expressions and a laptop.
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Automated Prediction of Preferences Using Facial Expressions
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Facial expression is a powerful non verbal communication cue beyond natural language.
“The expression of the Emotions in Man and Animals”. Charles Darwin
Paul Ekman. Universality of Facial expression. 6 Universal human emotions.
“We need to give computers the capacity to read our feelings and react, in ways that
have come to seem startlingly human.” Rana El Kaliouby
1.1. Detailed problem
Automated Prediction of Preferences Using Facial Expressions
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FACS 46 atomic movements of facial muscles.
Action Units = Building blocks of the emotions
Facial emotions = combinations of AUs
1.1. Detailed problem
Applications: We transmit more data with our expressions than with what we say.
Facial expressions predict: the result of a negotiation, the winner of a congress elections,
the election of a partner,…
GOAL: apply Computer Vision algorithms to model user preferences
in non-posed scenarios.
Automated Prediction of Preferences Using Facial Expressions
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Which cartoon this student enjoyed the most?
Automated Prediction of Preferences Using Facial Expressions
Methods: target phase
Target phase: Students evaluate two visual stimuli. [People,
Cartoons, Animals, Paintings]
Visualization for 3 seconds in a screen (E-Prime SW)
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Automated Prediction of Preferences Using Facial Expressions
Methods: perceiver phase
Students infer the preferences of other students from covertly
recorded videos of target’s faces.
They only see the target’s face, and guess the preference
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Automated Prediction of Preferences Using Facial Expressions
Methods: Algorithm
Facial landmarks detection per frame
Dispersion descriptor based on temporal landmarks displacements
Model:
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Face Detection
Mesh Fitting
Dynamic Landmark Descriptor
Classifier
Automated Prediction of Preferences Using Facial Expressions
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Results
Hypothesis one: Maximum – minimum displacement
JESP is human accuracy
Automated Prediction of Preferences Using Facial Expressions
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Results
Hypothesis two: apply SVM to the 66 differences between the
maximum and minimum centered displacement.
SVM: Standard Machine Learning classifier on a 132 dimensional space
Automated Prediction of Preferences Using Facial Expressions
Conclusions
Complex subtle emotion reading technology
Can we read minds from facial expression?
Yes, if we lower the expectations [Binary preference selection problem]
Applications:
- Depression monitorization
- Pain detection
- Deception detection
- Dynamically price advertising depending on how people responded to it,
- Autism
- Sony, Yahoo!, Motorola, Verizon, …
“Research in this area provides a rare point of convergence
between Computer Science and Social Psychology”
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Automated Prediction of Preferences Using Facial Expressions