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.
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Facial Feature Detection LeventeSajó 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 • 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
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
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
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
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
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
Distance Template • By replacing the feature points, the overall penalty of the template can be minimized
Conclusion • Emotion detection is a complex task • Single techniques proved to have several weaknesses • Combination of techniques can result a robust emotion detection