  We present a novel approach for supervised domain adaptation that is based
upon the probabilistic framework of Gaussian processes (GPs). Specifically, we
introduce domain-specific GPs as local experts for facial expression
classification from face images. The adaptation of the classifier is
facilitated in probabilistic fashion by conditioning the target expert on
multiple source experts. Furthermore, in contrast to existing adaptation
approaches, we also learn a target expert from available target data solely.
Then, a single and confident classifier is obtained by combining the
predictions from multiple experts based on their confidence. Learning of the
model is efficient and requires no retraining/reweighting of the source
classifiers. We evaluate the proposed approach on two publicly available
datasets for multi-class (MultiPIE) and multi-label (DISFA) facial expression
classification. To this end, we perform adaptation of two contextual factors:
'where' (view) and 'who' (subject). We show in our experiments that the
proposed approach consistently outperforms both source and target classifiers,
while using as few as 30 target examples. It also outperforms the
state-of-the-art approaches for supervised domain adaptation.
