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.

中文翻译：

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通过结构化秩最小化进行动态行为分析

人类行为和情感本质上是一种动态现象，涉及模式的时间演变，通过多种非语言行为线索表现出来，包括面部表情、身体姿势和手势以及声音爆发。人类行为建模的一个自然假设是，当行为线索作为输入时，行为的连续时间表征是线性时不变系统的输出（例如，维度影响的连续而不是离散注释）。在这里，我们通过采用结构化秩最小化来研究在现实世界条件下这种动态系统的学习，即在存在嘈杂的行为线索描述符和可能不可靠的注释的情况下。为此，提出了一种新颖的结构化秩最小化方法及其可扩展变体。通过对 3 个不同的动态行为分析任务进行实验，即（i）冲突强度预测，（ii）效价和唤醒预测，以及（iii）轨迹匹配，证明了所提出框架的普遍性。获得的结果优于其他最先进的方法在这些任务中取得的结果，因此证明了所提出方法的稳健性和有效性。