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Facial Feature Detection. Levente Sajó University of Debrecen. Human Computer Interaction. In multi-modal human-computer interaction takes an important part face detection/recognition extracting facial features emotion detection age recognition. Face Detection. - PowerPoint PPT Presentation
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Facial Feature DetectionFacial Feature Detection
Levente SajóUniversity of Debrecen
04.07.2008CSCS
2
Introduction
Emotion Detection
Feature Localization
Shape Templates
Human Computer InteractionHuman Computer Interaction
• In multi-modal human-computer interaction takes an important part– face detection/recognition– extracting facial features– emotion detection – age recognition
04.07.2008CSCS
3
Introduction
Emotion Detection
Feature Localization
Shape Templates
Face DetectionFace Detection
• 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
04.07.2008CSCS
4
Introduction
Emotion Detection
Feature Localization
Shape Templates
Emotion DetectionEmotion Detection
• 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
04.07.2008CSCS
5
Introduction
Emotion Detection
Feature Localization
Shape Templates
Emotion DetectionEmotion Detection
• 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
04.07.2008CSCS
6
Introduction
Emotion Detection
Feature Localization
Shape Templates
Localizing Facial FeaturesLocalizing Facial Features
• 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
04.07.2008CSCS
7
Introduction
Emotion Detection
Feature Localization
Shape Templates
Localizing Facial FeaturesLocalizing Facial Features
• 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
04.07.2008CSCS
8
Introduction
Emotion Detection
Feature Localization
Shape Templates
Distance TemplateDistance Template
• 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
04.07.2008CSCS
9
Introduction
Emotion Detection
Feature Localization
Shape Templates
Distance TemplateDistance Template
• By replacing the feature points, the overall penalty of the template can be minimized
04.07.2008CSCS
10
Introduction
Emotion Detection
Feature Localization
Shape Templates
ConclusionConclusion
• Emotion detection is a complex task
• Single techniques proved to have several weaknesses
• Combination of techniques can result a robust emotion detection
Thanks for attention!Thanks for attention!