Facial Feature Detection

1. Facial Feature Detection LeventeSajó University of Debrecen

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

3. Face 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

4. Emotion 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

5. Emotion 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

6. Localizing 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

7. Localizing 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

8. Distance 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

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

10. Conclusion • 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!