Cross-Modal Phase Graphs for Separating Affect from Identity in Gait Sequences
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Abstract
Human gait contains a rich amalgamation of behavioral and biological signals, functioning simultaneously as a reliable biometric for identity verification and a subtle indicator of underlying affective states. However, isolating the emotional state from the structural identity in gait sequences remains a highly complex problem due to the profound kinematic entanglement of these two factors. This paper introduces the concept of cross-modal phase graphs, a novel representational framework designed specifically to disentangle affective features from identity features within dynamic gait sequences. By leveraging complementary information across visual silhouettes and three-dimensional skeletal joint coordinates, the proposed framework constructs temporal graphs partitioned according to the biomechanical phases of human walking, such as the stance and swing phases. Through a dual-branch separation mechanism relying entirely on spatial and temporal message passing, the architecture isolates structural invariances associated with identity from the dynamic, high-variance kinematic variations indicative of emotion. Extensive qualitative and quantitative analyses demonstrate the validity of this theoretical approach, revealing that cross-modal representations significantly alleviate the confounding effects of physical stature on emotion recognition. The findings establish a robust foundation for advancing non-intrusive surveillance, human-computer interaction, and affective computing systems in unconstrained environments.
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