Dynamic Behavior Analysis via Structured Rank Minimization

Christos Georgakis* (Corresponding Author), Yannis Panagakis, Maja Pantic

*Corresponding author for this work

    Research output: Contribution to journalArticleAcademicpeer-review

    6 Citations (Scopus)
    105 Downloads (Pure)


    Human behavior and affect is inherently a dynamic phenomenon involving temporal evolution of patterns manifested through a multiplicity of non-verbal behavioral cues including facial expressions, body postures and gestures, and vocal outbursts. A natural assumption for human behavior modeling is that a continuous-time characterization of behavior is the output of a linear time-invariant system when behavioral cues act as the input (e.g., continuous rather than discrete annotations of dimensional affect). Here we study the learning of such dynamical system under real-world conditions, namely in the presence of noisy behavioral cues descriptors and possibly unreliable annotations by employing structured rank minimization. To this end, a novel structured rank minimization method and its scalable variant are proposed. The generalizability of the proposed framework is demonstrated by conducting experiments on 3 distinct dynamic behavior analysis tasks, namely (i) conflict intensity prediction, (ii) prediction of valence and arousal, and (iii) tracklet matching. The attained results outperform those achieved by other state-of-the-art methods for these tasks and, hence, evidence the robustness and effectiveness of the proposed approach.

    Original languageEnglish
    Pages (from-to)333-357
    Number of pages25
    JournalInternational journal of computer vision
    Issue number2-4
    Publication statusPublished - 1 Apr 2018


    • UT-Hybrid-D
    • Hankel matrix
    • Linear time-invariant systems
    • Low-rank
    • Sparsity
    • Structured rank minimization
    • Dynamic behavior analysis


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