4 research outputs found
Dynamic Facial Landmarking Selection for Emotion Recognition using Gaussian Processes
Facial features are the basis for the emotion
recognition process and are widely used in affective
computing systems. This emotional process is produced
by a dynamic change in the physiological signals
and the visual answers related to the facial expressions.
An important factor in this process, relies
on the shape information of a facial expression, represented
as dynamically changing facial landmarks. In
this paper we present a framework for dynamic facial
landmarking selection based on facial expression analysis
using Gaussian Processes. We perform facial features
tracking, based on Active Appearance Models for
facial landmarking detection, and then use Gaussian
process ranking over the dynamic emotional sequences
with the aim to establish which landmarks are more
relevant for emotional multivariate time-series recognition.
The experimental results show that Gaussian Processes
can effectively fit to an emotional time-series and
the ranking process with log-likelihoods finds the best
landmarks (mouth and eyebrows regions) that represent
a given facial expression sequence. Finally, we use
the best ranked landmarks in emotion recognition tasks
obtaining accurate performances for acted and spontaneous
scenarios of emotional datasets