Natural human-computer interaction and audio-visual human behaviour sensing systems, which would achieve robust performance in-the-wild are more needed than ever as digital devices are increasingly becoming an indispensable part of our life. Accurately annotated real-world data are the crux in devising such systems. However, existing databases usually consider controlled settings, low demographic variability, and a single task. In this paper, we introduce the SEWA database of more than 2000 minutes of audio-visual data of 398 people coming from six cultures, 50% female, and uniformly spanning the age range of 18 to 65 years old. Subjects were recorded in two different contexts: while watching adverts and while discussing adverts in a video chat. The database includes rich annotations of the recordings in terms of facial landmarks, facial action units (FAU), various vocalisations, mirroring, and continuously valued valence, arousal, liking, agreement, and prototypic examples of (dis)liking. This database aims to be an extremely valuable resource for researchers in affective computing and automatic human sensing and is expected to push forward the research in human behaviour analysis, including cultural studies. Along with the database, we provide extensive baseline experiments for automatic FAU detection and automatic valence, arousal and (dis)liking intensity estimation.

We consider the task of automated estimation of facial expression intensity. This involves estimation of multiple output variables (facial action units -AUs) that are structurally dependent. Their structure arises from statistically induced co-occurrence patterns of AU intensity levels. Modeling this structure is critical for improving the estimation performance; however, this performance is bounded by the quality of the input features extracted from face images. The goal of this paper is to model these structures and estimate complex feature representations simultaneously by combining conditional random field (CRF) encoded AU dependencies with deep learning. To this end, we propose a novel Copula CNN deep learning approach for modeling multivariate ordinal variables. Our model accounts for ordinal structure in output variables and their non-linear dependencies via copula functions modeled as cliques of a CRF. These are jointly optimized with deep CNN feature encoding layers using a newly introduced balanced batch iterative training algorithm. We demonstrate the effectiveness of our approach on the task of AU intensity estimation on two benchmark datasets. We show that joint learning of the deep features and the target output structure results in significant performance gains compared to existing deep structured models for analysis of facial expressions.

Automated recognition of facial expressions of emotions, and detection of facial action units (AUs), from videos depends critically on modeling of their dynamics. These dynamics are characterized by changes in temporal phases (onset-apex-offset) and intensity of emotion expressions and AUs, the appearance of which may vary considerably among target subjects, making the recognition/detection task very challenging. The state-of-the-art Latent Conditional Random Fields (L-CRF) framework allows one to efficiently encode these dynamics through the latent states accounting for the temporal consistency in emotion expression and ordinal relationships between its intensity levels, these latent states are typically assumed to be either unordered (nominal) or fully ordered (ordinal). Yet, such an approach is often too restrictive. For instance, in the case of AU detection, the goal is to discriminate between the segments of an image sequence in which this AU is active or inactive. While the sequence segments containing activation of the target AU may better be described using ordinal latent states (corresponding to the AU intensity levels), the inactive segments (i.e., where this AU does not occur) may better be described using unordered (nominal) latent states, as no assumption can be made about their underlying structure (since they can contain either neutral faces or activations of non-target AUs). To address this, we propose the variable-state L-CRF (VSL-CRF) model that automatically selects the optimal latent states for the target image sequence, based on the input data and underlying dynamics of the sequence. To reduce the model overfitting either the nominal or ordinal latent states, we propose a novel graph-Laplacian regularization of the latent states. We evaluate the VSL-CRF on the tasks of facial expression recognition using the CK+ dataset, and AU detection using the GEMEP-FERA and DISFA datasets, and show that the proposed model achieves better generalization performance compared to traditional L-CRFs and other related state-of-the-art models.

Joint modeling of the intensity of facial action units (AUs) from face images is challenging due to the large number of AUs (30+) and their intensity levels (6). This is in part due to the lack of suitable models that can efficiently handle such a large number of outputs/classes simultaneously, but also due to the lack of labelled target data. For this reason, majority of the methods proposed so far resort to independent classifiers for the AU intensity. This is suboptimal for at least two reasons: the facial appearance of some AUs changes depending on the intensity of other AUs, and some AUs co-occur more often than others. Encoding this is expected to improve the estimation of target AU intensities, especially in the case of noisy image features, head-pose variations and imbalanced training data. To this end, we introduce a novel modeling framework, Copula Ordinal Regression (COR), that leverages the power of copula functions and CRFs, to detangle the probabilistic modeling of AU dependencies from the marginal modeling of the AU intensity. Consequently, the COR model achieves the joint learning and inference of intensities of multiple AUs, while being computationally tractable. We show on two challenging datasets of naturalistic facial expressions that the proposed approach consistently outperforms (i) independent modeling of AU intensities, and (ii) the state-ofthe-art approach for the target task.

Human face exhibits an inherent hierarchy in its representations (i.e., holistic facial expressions can be encoded via a set of facial action units (AUs) and their intensity). Variational (deep) auto-encoders (VAE) have shown great results in unsupervised extraction of hierarchical latent representations from large amounts of image data, while being robust to noise and other undesired artifacts. Potentially, this makes VAEs a suitable approach for learning facial features for AU intensity estimation. Yet, most existing VAE-based methods apply classifiers learned separately from the encoded features. By contrast, the non-parametric (probabilistic) approaches, such as Gaussian Processes (GPs), typically outperform their parametric counterparts, but cannot deal easily with large amounts of data. To this end, we propose a novel VAE semi-parametric modeling framework, named DeepCoder, which combines the modeling power of parametric (convolutional) and nonparametric (ordinal GPs) VAEs, for joint learning of (1) latent representations at multiple levels in a task hierarchy 1 , and (2) classification of multiple ordinal outputs. We show on benchmark datasets for AU intensity estimation that the proposed DeepCoder outperforms the state-of-the-art approaches, and related VAEs and deep learning models.

Automated recognition of facial expressions of emotions, and detection of facial action units (AUs), from videos depends critically on modeling of their dynamics. These dynamics are characterized by changes in temporal phases (onset-apex-offset) and intensity of emotion expressions and AUs, the appearance of which may vary considerably among target subjects, making the recognition/detection task very challenging. The state-of-the-art Latent Conditional Random Fields (L-CRF) framework allows one to efficiently encode these dynamics through the latent states accounting for the temporal consistency in emotion expression and ordinal relationships between its intensity levels, these latent states are typically assumed to be either unordered (nominal) or fully ordered (ordinal). Yet, such an approach is often too restrictive. For instance, in the case of AU detection, the goal is to discriminate between the segments of an image sequence in which this AU is active or inactive. While the sequence segments containing activation of the target AU may better be described using ordinal latent states (corresponding to the AU intensity levels), the inactive segments (i.e., where this AU does not occur) may better be described using unordered (nominal) latent states, as no assumption can be made about their underlying structure (since they can contain either neutral faces or activations of non-target AUs). To address this, we propose the variable-state L-CRF (VSL-CRF) model that automatically selects the optimal latent states for the target image sequence, based on the input data and underlying dynamics of the sequence. To reduce the model overfitting either the nominal or ordinal latent states, we propose a novel graph-Laplacian regularization of the latent states. We evaluate the VSL-CRF on the tasks of facial expression recognition using the CK+ dataset, and AU detection using the GEMEP-FERA and DISFA datasets, and show that the proposed model achieves better generalization performance compared to traditional L-CRFs and other related state-of-the-art models.

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