1. AHED Automatic Human Emotion Detection

2. I am Retshidisitswe Lehata
Hello!
I am Retshidisitswe Lehata
Supervisor:
Mehrdad Ghaziasgar
Co-Supervisor:
Reg Dodds

3. Background Overview of the System Training the System SVM Optimization
Feature Optimization
Test Parameters
Test Dataset
Test Results
Tools Used
References
Demo
Add optimization

4. Customer service is a large revenue stream for some companies
Background
Customer service is a large revenue stream for some companies
Not all customers readily express their emotions verbally
A dissatisfied customer is more likely to walk away do their business elsewhere than complain

5. Real time emotion classification Emoji – Dominant emotion
Overview of the System
Live video stream
Real time emotion classification
Emoji – Dominant emotion
Probability of all 7 emotions

6. Training the System

7. Mean Cross-Validation score: 84%
SVM Optimization
Grid Search:
RBF kernel
C and Gamma
Linear kernel C
C – (2-5,215)
Gamma - (2-15, 23)
Cross-Validation:
Stratified K fold
3 Folds
Mean Cross-Validation score: 84%
Where C determines the extent to which the SVM model should avoid misclassication
in the training.
Gamma?
RBF?
Linear?

8. Accuracy of HOG Parameters
Feature Optimization
Accuracy of HOG Parameters
Cell (8; 8) Block (3; 3)
Cell (4; 4) Block (3; 3)
OpenCV HOG
86%
88.2%
AHED HOG
77.9%
75.7%
Cell (8; 8) Block (2; 2)
Cell (4; 4) Block (2; 2)
83.8%
82.3%

9. (0o-180o) Unsigned Gradients
Test Parameters
Parameter
Size
Type
Input Image
Image
(56, 56)
Pixels
HOG Parameters
Cell
(4, 4)
Block
(3, 3)
Cells
Overlap
66.66%
Blocks
Bins 9
(0o-180o) Unsigned Gradients
Feature Vector Size: 11664

10. 40% of CK+ Dataset 136 test cases 83 test subjects 7 emotions
Test Dataset
40% of CK+ Dataset
136 test cases
83 test subjects
7 emotions

11. Different genders and races
Test Dataset
Sample of CK+ Dataset
Different genders and races

12. Figure: Confusion Matrix
Test Results
Sample
Correct Predictions
F1-Score (accuracy)
Angry 17 14
78%
Disgust 24 22
96%
Fear 10 6
71%
Happy 27
Neutral
69%
Sad 11 9
Surprise 33 32
98%
136
120
88%
F1 – Score – accuracy of the predictions
Recall – True positive rate
Precision – Positive prediction rate
Figure: Confusion Matrix

13. Tools Used

14. References
P. Viola and M. J. Jones. Robust real-time face detection. International Journal of Computer Vision, 2(57): , 2004.
N. Dalal and B. Triggs. Histograms of oriented gradients for human detection. Computer Vision and Pattern Recognition, CVPR IEEE Computer Society Conference, 1: , 2005.
Chih-Wei Hsu, Chih-Chung Chang, Chih-Jen Lin, et al. A practical guide to support vector classification

15. Demo
Angry
Disgust
Fear
Happy
Neutral
Sad
Surprise