1

THE EXTENDED COHN-KANADE

DATASET(CK+):A COMPLETE DATASET

FOR ACTION UNIT AND EMOTION-

SPECIFIED 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

2

Outline

• Introduction

• The CK+ Dataset

• Emotion Labels

• Baseline System

• Experiments

• Conclusion

3

Introduction

• In 2000, the Cohn-Kanade (CK) database was released

• Automatically detecting facial expressions has become an

increasingly important research area

4

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).

5

Introduction(Cont.)

• Facial Action Coding System (FACS) is a system to

taxonomize human facial movements by their appearance

on the face

6

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

7

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.

8

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.

9

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.

10

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.

11

Emotion Labels(Cont.)

12

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.

13

Baseline System

14

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]

15

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

16

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.

17

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.

18

Experiments(Cont.)• SPTS

19

Experiments(Cont.)• CAPP

20

Experiments(Cont.)• SPTS+CAPP

21

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

22

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.