<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Facial features tracking is widely used in face recognition, gesture, expression analysis, etc. AAM (Active Appearance Model) is one of the powerful methods for objects feature localization. Nevertheless, AAM still suffers from a few drawbacks, such as the view angle change problem. We present a method to solve it by using the depth data acquired from Kinect. We use the depth data to get the head pose information and RGB data to match the AAM result. We establish an approximate facial 3D gird model and then initialize the subsequent frames with this model and head pose information. To avoid the local extremum, we divide the model into several parts by the poses and match the facial features with the closest model. The experimental results show improvement of AAM performance when rotating the head.</p></abstract>

Facial features tracking is widely used in face recognition, gesture, expression analysis, etc. AAM (Active Appearance Model) is one of the powerful methods for objects feature localization. Nevertheless, AAM still suffers from a few drawbacks, such as the view angle change problem. We present a method to solve it by using the depth data acquired from Kinect. We use the depth data to get the head pose information and RGB data to match the AAM result. We establish an approximate facial 3D gird model and then initialize the subsequent frames with this model and head pose information. To avoid the local extremum, we divide the model into several parts by the poses and match the facial features with the closest model. The experimental results show improvement of AAM performance when rotating the head.

AAM Based Facial Feature Tracking with Kinect

School of Information, Qilu University of Technology, China

Basis Medical College, Shandong University of Traditional Chinese Medicine, China

Cybernetics and Information Technologies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"AAM Based Facial Feature Tracking with Kinect"}

Facial features tracking is widely used in face recognition, gesture, expression analysis, etc. AAM (Active Appearance Model) is one of the powerful methods...

AAM Based Facial Feature Tracking with Kinect

Online veröffentlicht: 05. Okt. 2015

Seitenbereich: 127 - 139

DOI: https://doi.org/10.1515/cait-2015-0046

Schlüsselwörter
Facial feature tracking, active appearance model, view based model, Kinect

© Qingxiang Wang et al.

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

AAM Based Facial Feature Tracking with Kinect

Online veröffentlicht: 05. Okt. 2015

Seitenbereich: 127 - 139

DOI: https://doi.org/10.1515/cait-2015-0046

SchlüsselwörterFacial feature tracking, active appearance model, view based model, Kinect

© Qingxiang Wang et al.

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
Facial feature tracking, active appearance model, view based model, Kinect