Joint Facial Action Unit Detection and Feature Fusion: A Multi-Conditional Learning Approach

Stefanos Eleftheriadis, Ognjen Rudovic, Maja Pantic

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    14 Citations (Scopus)
    2 Downloads (Pure)


    Automated analysis of facial expressions can benefit many domains, from marketing to clinical diagnosis of neurodevelopmental disorders. Facial expressions are typically encoded as a combination of facial muscle activations, i.e., action units. Depending on context, these action units co-occur in specific patterns, and rarely in isolation. Yet, most existing methods for automatic action unit detection fail to exploit dependencies among them, and the corresponding facial features. To address this, we propose a novel multi-conditional latent variable model for simultaneous fusion of facial features and joint action unit detection. In particular, the proposed model performs feature fusion in a generative fashion via a low-dimensional shared subspace, while simultaneously performing action unit detection using a discriminative classification approach. We show that by combining the merits of both approaches, the proposed methodology outperforms existing purely discriminative/generative methods for the target task. To reduce the number of parameters, and avoid overfitting, a novel Bayesian learning approach based on Monte Carlo sampling is proposed, to integrate out the shared subspace. We validate the proposed method on posed and spontaneous data from three publicly available data sets (CK+, DISFA, and Shoulder-pain), and show that both feature fusion and joint learning of action units leads to improved performance compared with the state-of-the-art methods for the task.
    Original languageUndefined
    Pages (from-to)5727-5742
    Number of pages16
    JournalIEEE transactions on image processing
    Issue number12
    Publication statusPublished - Dec 2016


    • EWI-27583
    • HMI-HF: Human Factors
    • multi-conditional learning
    • IR-103795
    • EC Grant Agreement nr.: FP7/611153
    • Gaussian processes
    • Multiple action unit detection
    • multi-label

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