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

Human Computer Interaction 04.07.2008 CSCS In multi-modal human-computer interaction takes an important part face detection/recognition extracting facial features emotion detection age recognition

Face Detection 04.07.2008 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

Emotion Detection 04.07.2008 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

Emotion Detection 04.07.2008 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

Localizing Facial Features 04.07.2008 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

Localizing Facial Features 04.07.2008 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

Distance Template The template is described by template rules 04.07.2008 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

Distance Template 04.07.2008 CSCS By replacing the feature points, the overall penalty of the template can be minimized

Conclusion Emotion detection is a complex task 04.07.2008 CSCS 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! 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