1. Facial Feature Detection
Levente Sajó
University of Debrecen
Change the menu items in View -> Master -> Slide Master view (fix the menu on the first slide, and then paste the text as unformatted, and remove the unnecessary menu items, it is possible that the activ menu item should be re-colored to light gray manually). Take care of the invisible hotspot buttons which link to the slide numbers. Better to use this template always for a new presentation. It is tricky to click on the menu text bob in the master slides, somewhere above the first hotspot. Then click somewhere between two menu items to edit it.
Use Insert -> Duplicate slides when making new slides

2. Human Computer Interaction
CSCS
In multi-modal human-computer interaction takes an important part
face detection/recognition
extracting facial features
emotion detection
age recognition

3. Face Detection
CSCS
For detecting faces, many different techniques appeared over the years
Template based
Appearance based (neural networks, SVM)
Probably the most successful is the one based on cascaded Haar-classifiers (Boosted Cascade Detector - BCD)
On the localized face further steps can be performed for recognizing gender, age or facial gestures

4. Emotion Detection
CSCS
6 different facial emotions: neutral, happy, sad, surprised, angry, fear, disgust
Classification methods used in face detection can be used for emotion detection, too:
Gabor-transformed image is classified using SVM or BCD
A feature vector formed by manually defined facial landmarks is passed to SVM classifier

5. Emotion Detection
CSCS
Emotion detection is sensitive for changes of illumination and different rotation of the face
Using 2 cameras, 3D feature points can be used for constructing the feature vectors, with these more accurate classifiers can be created

6. Localizing Facial Features
CSCS
Local feature detectors (SVM, BCD) can be used to detect facial features
Since facial features contains less information then the whole face, individual feature detectors seemed to be unreliable

7. Localizing Facial Features
CSCS
Shape models can be used to
reduce the number of false detections by only selecting plausible configurations of feature matches
correcting the false detection of the local feature detectors
Statistical Shape Model
For each landmarks their mean position and variance are determined
Distance Shape Template

8. Distance Template The template is described by template rules
CSCS
The template is described by template rules
A rule defines the estimated distance between template points
If a template point does not satisfy the conditions of a rule, a penalty value is calculated
The sum of the penalties gives the overall penalty of the template

9. Distance Template
CSCS
By replacing the feature points, the overall penalty of the template can be minimized

10. Conclusion Emotion detection is a complex task
CSCS
Emotion detection is a complex task
Single techniques proved to have several weaknesses
Combination of techniques can result a robust emotion detection

11. Thanks for attention!
Change the menu items in View -> Master -> Slide Master view (fix the menu on the first slide, and then paste the text as unformatted, and remove the unnecessary menu items, it is possible that the activ menu item should be re-colored to light gray manually). Take care of the invisible hotspot buttons which link to the slide numbers. Better to use this template always for a new presentation. It is tricky to click on the menu text bob in the master slides, somewhere above the first hotspot. Then click somewhere between two menu items to edit it.
Use Insert -> Duplicate slides when making new slides