Emotional Expression Classification using Time-Series Kernels
This work addresses facial expression recognition for applications like human-computer interaction, but it is incremental as it applies existing kernel methods to a specific domain.
The paper tackled the problem of classifying facial expressions by using kernel methods with dynamic time-warping similarity measures on 3D motion patterns, achieving over 99% accuracy in full motion classification and over 90% accuracy in recognizing expressions from as few as 5-6 frames (about 200 milliseconds).
Estimation of facial expressions, as spatio-temporal processes, can take advantage of kernel methods if one considers facial landmark positions and their motion in 3D space. We applied support vector classification with kernels derived from dynamic time-warping similarity measures. We achieved over 99% accuracy - measured by area under ROC curve - using only the 'motion pattern' of the PCA compressed representation of the marker point vector, the so-called shape parameters. Beyond the classification of full motion patterns, several expressions were recognized with over 90% accuracy in as few as 5-6 frames from their onset, about 200 milliseconds.