Learning person-specific models for facial expression and action unit recognition

Chen, Jixu; Liu, Xiaoming; Tu, Peter; Aragones, Amy

doi:10.1016/j.patrec.2013.02.002

Cited by 81 publications

(93 citation statements)

References 25 publications

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“…In [22], a two-step learning approach is proposed for personspecific pain recognition and AU detection. First, data of each subject are regarded as different source domains, and are used to train weak Adaboost classifiers.…”

Section: A Domain Adaptation In Facial Behavior Analysismentioning

confidence: 99%

“…Note that, apart from [22], all the works mentioned above operate in the unsupervised setting. While this requires less effort in terms of obtaining the labels for the target subsample, its underlying assumption is that target data can be well represented as a weighted combination of the source data.…”

Section: A Domain Adaptation In Facial Behavior Analysismentioning

confidence: 99%

“…1 More specifically, instead of adjusting the classifier parameters between the domains, as in [10], [21], [22], [23], [11], we propose the use of domainspecific GP experts that model the domain specific attributes. The modeling power of GPs allows us to model the desired attributes in the target domain, in a data-efficient manner.…”

Section: Introductionmentioning

confidence: 99%

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Gaussian Process Domain Experts for Modeling of Facial Affect

Eleftheriadis

Rudovic

Deisenroth

et al. 2017

IEEE Trans. on Image Process.

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Abstract-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, etc. 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 datasets 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 (i) both source and target classifiers, while using a small number of target examples during the adaptation, and (ii) related state-ofthe-art approaches for supervised domain adaptation.

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Section: A Domain Adaptation In Facial Behavior Analysismentioning

confidence: 99%

Section: A Domain Adaptation In Facial Behavior Analysismentioning

confidence: 99%

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Gaussian Process Domain Experts for Modeling of Facial Affect

Eleftheriadis

Rudovic

Deisenroth

et al. 2017

IEEE Trans. on Image Process.

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“…The personalized models can be divided into three groups: the person-dependent models ( [8,9,10]), the person-adaptable models ( [11,12,13,14]), and the models that use personalized facial features ( [15]). The first group uses data of both the training and test persons during learning, and these models are typically tailored to each person (training and test).…”

Section: Introductionmentioning

confidence: 99%

“…By comparing the models on the pain expression recognition task, the proposed adaptable models outperformed generic models based on the subtractive method. Similarly, [13] proposed two transfer learning algorithms: inductive and transductive transfer learning for detection of pain and AUs, in both a semi-supervised and unsupervised settings. [16] proposed a personalized model for AU detection that is based on the Kernel Mean Matching technique.…”

Section: Introductionmentioning

confidence: 99%

Personalized Modeling of Facial Action Unit Intensity

Yang

Rudovic

Pavlović

et al. 2014

Advances in Visual Computing

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Abstract. Facial expressions depend greatly on facial morphology and expressiveness of the observed person. Recent studies have shown great improvement of the personalized over non-personalized models in variety of facial expression related tasks, such as face and emotion recognition. However, in the context of facial action unit (AU) intensity estimation, personalized modeling has been scarcely investigated. In this paper, we propose a two-step approach for personalized modeling of facial AU intensity from spontaneously displayed facial expressions. In the first step, we perform facial feature decomposition using the proposed matrix decomposition algorithm that separates the person's identity from facial expression. These two are then jointly modeled using the framework of Conditional Ordinal Random Fields, resulting in a personalized model for intensity estimation of AUs. Our experimental results show that the proposed personalized model largely outperforms non-personalized models for intensity estimation of AUs.

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