@inproceedings{Eleftheriadis:2016:10.1007/978-3-319-54184-6_10,
author = {Eleftheriadis, S and Rudovic, O and Deisenroth, MP and Pantic, M},
doi = {10.1007/978-3-319-54184-6_10},
pages = {154--170},
publisher = {Springer},
title = {Variational Gaussian Process Auto-Encoder for Ordinal Prediction of Facial Action Units},
url = {http://dx.doi.org/10.1007/978-3-319-54184-6_10},
year = {2016}
}

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TY  - CPAPER
AB  - We address the task of simultaneous feature fusion and modelingof discrete ordinal outputs. We propose a novel Gaussian process(GP) auto-encoder modeling approach. In particular, we introduce GPencoders to project multiple observed features onto a latent space, whileGP decoders are responsible for reconstructing the original features. Inferenceis performed in a novel variational framework, where the recoveredlatent representations are further constrained by the ordinal outputlabels. In this way, we seamlessly integrate the ordinal structure in thelearned manifold, while attaining robust fusion of the input features.We demonstrate the representation abilities of our model on benchmarkdatasets from machine learning and affect analysis. We further evaluatethe model on the tasks of feature fusion and joint ordinal predictionof facial action units. Our experiments demonstrate the benefits of theproposed approach compared to the state of the art.
AU  - Eleftheriadis,S
AU  - Rudovic,O
AU  - Deisenroth,MP
AU  - Pantic,M
DO  - 10.1007/978-3-319-54184-6_10
EP  - 170
PB  - Springer
PY  - 2016///
SN  - 0302-9743
SP  - 154
TI  - Variational Gaussian Process Auto-Encoder for Ordinal Prediction of Facial Action Units
UR  - http://dx.doi.org/10.1007/978-3-319-54184-6_10
UR  - http://hdl.handle.net/10044/1/40069
ER  - 

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