The Extended Cohn-Kanade Dataset(CK+):A complete dataset for

Report
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THE EXTENDED COHN-KANADE
DATASET(CK+):A COMPLETE DATASET
FOR ACTION UNIT AND EMOTIONSPECIFIED EXPRESSION
Author:Patrick Lucey, Jeffrey F. Cohn, Takeo
Kanade, Jason Saragih, Zara Ambadar
Conference on Computer Vision and Pattern
Recognition 2010
Speaker:Liu, Yi-Hsien
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Outline
• Introduction
• The CK+ Dataset
• Emotion Labels
• Baseline System
• Experiments
• Conclusion
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Introduction
• In 2000, the Cohn-Kanade (CK) database was released
• Automatically detecting facial expressions has become an
increasingly important research area
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Introduction(Cont.)
• The CK database contains 486 sequences across 97
subjects.
• Each of the sequences contains images from onset
(neutral frame) to peak expression (last frame).
• The peak frame was reliably FACS(Facial Action Coding
System ) coded for facial action units (AUs).
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Introduction(Cont.)
• Facial Action Coding System (FACS) is a system to
taxonomize human facial movements by their appearance
on the face
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Introduction(Cont.)
• While AU codes are well validated, emotion labels are not
• The lack of a common performance metric against which
to evaluate new algorithms
• Standard protocols for common databases have not
emerged
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The CK+ Dataset
• Participants were 18 to 50 years of age, 69% female, 81%
Euro-American, 13% Afro-American, and 6% other groups
• Image sequences for frontal views and 30-degree views
were digitized into either 640x490 or 640x480 pixel arrays
with 8- bit gray-scale or 24-bit color values.
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The CK+ Dataset(Cont.)
• For the CK+ distribution, they have augmented the
dataset further to include 593 sequences from 123
subjects (an additional 107 (22%) sequences and 26
(27%) subjects).
• For the 593 posed sequences, full FACS coding of peak
frames is provided.
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Emotion Labels
• They included all image data from the pool of 593
sequences that had a nominal emotion label based on the
subject’s impression of each of the 7 basic emotion
categories: Anger, Contempt, Disgust, Fear, Happy,
Sadness and Surprise.
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Emotion Labels(Cont.)
1.
Compared the FACS codes with the Emotion Prediction
Table from the FACS
2.
After the first pass, a more loose comparison was
performed
3.
The third step involved perceptual judgment of whether
or not the expression resembled the target emotion
category.
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Emotion Labels(Cont.)
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Emotion Labels(Cont.)
• As a result of this multistep selection process, 327 of the
593 sequences were found to meet criteria for one of
seven discrete emotions.
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Baseline System
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Baseline System(Cont.)
• Active Appearance Models (AAMs)
• The shape s of an AAM is described by a 2D triangulated
mesh.
• In particular, the coordinates of the mesh vertices define
the shape s = [x1; y1; x2; y2; …. ; xn; yn]
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Baseline System(Cont.)
• SPTS:The similarity normalized shape, refers to the 68
vertex points for both the x- and y- coordinates, resulting
in a raw 136 dimensional feature vector
• CAPP: The canonical normalized appearance, refers to
where all the shape variation has been normalized with
respect to the base shape
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Baseline System(Cont.)
• SVMs(Support Vector Machines) attempt to find the hyper
plane that maximizes the margin between positive and
negative observations for a specified class.
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Experiments
• Emotion detection.
• To maximize the amount of training and testing data, they
believe the use of a leave-one-subject-out cross-
validation configuration should be used.
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Experiments(Cont.)
• SPTS
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Experiments(Cont.)
• CAPP
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Experiments(Cont.)
• SPTS+CAPP
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Conclusion
• In this paper, they try to address those three issues by
presenting the Extended Cohn-Kanade (CK+) database
• Added another 107 sequences as well as another 26
subjects.
• The peak expression for each sequence is fully FACS
coded and emotion labels have been revised and
validated
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Conclusion(Cont.)
• Propose the use of a leave-one-out subject cross-
validation strategy for evaluating performance
• Present baseline results on this using our Active
Appearance Model (AAM)/support vector machine (SVM)
system.

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