In this section
@article{Eleftheriadis:2017:10.1109/TIP.2017.2721114,
author = {Eleftheriadis, S and Rudovic, O and Deisenroth, MP and Pantic, M},
doi = {10.1109/TIP.2017.2721114},
journal = {IEEE Transactions on Image Processing},
pages = {4697--4711},
title = {Gaussian process domain experts for modeling of facial affect},
url = {http://dx.doi.org/10.1109/TIP.2017.2721114},
volume = {26},
year = {2017}
}
TY - JOUR
AB - Most of existing models for facial behavior analysis rely on generic classifiers, which fail to generalize well to previously unseen data. This is because of inherent differences in source (training) and target (test) data, mainly caused by variation in subjects’ facial morphology, camera views, and so on. All of these account for different contexts in which target and source data are recorded, and thus, may adversely affect the performance of the models learned solely from source data. In this paper, we exploit the notion of domain adaptation and propose a data efficient approach to adapt already learned classifiers to new unseen contexts. Specifically, we build upon the probabilistic framework of Gaussian processes (GPs), and introduce domain-specific GP experts (e.g., for each subject). The model adaptation is facilitated in a probabilistic fashion, by conditioning the target expert on the predictions from multiple source experts. We further exploit the predictive variance of each expert to define an optimal weighting during inference. We evaluate the proposed model on three publicly available data sets for multi-class (MultiPIE) and multi-label (DISFA, FERA2015) facial expression analysis by performing adaptation of two contextual factors: “where” (view) and “who” (subject). In our experiments, the proposed approach consistently outperforms: 1) both source and target classifiers, while using a small number of target examples during the adaptation and 2) related state-of-the-art approaches for supervised domain adaptation.
AU - Eleftheriadis,S
AU - Rudovic,O
AU - Deisenroth,MP
AU - Pantic,M
DO - 10.1109/TIP.2017.2721114
EP - 4711
PY - 2017///
SN - 1941-0042
SP - 4697
TI - Gaussian process domain experts for modeling of facial affect
T2 - IEEE Transactions on Image Processing
UR - http://dx.doi.org/10.1109/TIP.2017.2721114
UR - http://hdl.handle.net/10044/1/49121
VL - 26
ER -
Artificial Intelligence Network
South Kensington Campus
Imperial College London
SW7 2AZ
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