Facial expression identification is an important part of face recognition and closely related to emotion detection from
face images. Various solutions have been proposed in the past using different types of cameras and features. Microsoft
Kinect device has been widely used for multimedia interactions. More recently, the device has been increasingly
deployed for supporting scientific investigations. This paper explores the effectiveness of using the device in identifying
emotional facial expressions such as surprise, smile, sad, etc. and evaluates the usefulness of 3D data points on a face
mesh structure obtained from the Kinect device. We present a distance-based geometric feature component that is
derived from the distances between points on the face mesh and selected reference points in a single frame. The feature
components extracted across a sequence of frames starting and ending by neutral emotion represent a whole expression.
The feature vector eliminates the need for complex face orientation correction, simplifying the feature extraction process
and making it more efficient. We applied the kNN classifier that exploits a feature component based similarity measure
following the principle of dynamic time warping to determine the closest neighbors. Preliminary tests on a small scale
database of different facial expressions show promises of the newly developed features and the usefulness of the Kinect
device in facial expression identification.
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