Gaussian process domain experts for model adaptation in facial behavior analysis

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Title: Gaussian process domain experts for model adaptation in facial behavior analysis
Authors: Eleftheriadis, S
Rudovic, O
Deisenroth, MP
Pantic, M
Item Type: Conference Paper
Abstract: 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.
Issue Date: 31-Dec-2016
Date of Acceptance: 27-Apr-2016
URI: http://hdl.handle.net/10044/1/32006
Publisher: IEEE
Journal / Book Title: Proceedings of IEEE CVPR 2016
Copyright Statement: © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Conference Name: Fourth International Workshop on Context Based Affect Recognition 2016
Keywords: stat.ML
cs.CV
cs.LG
Publication Status: Accepted
Start Date: 2016-06-26
Finish Date: 2016-07-01
Conference Place: Las Vegas, Nevada, USA
Open Access location: http://arxiv.org/abs/1604.02917
Appears in Collections:Faculty of Engineering
Computing



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