Abstract
Eye Tracking (ET) parameters, as physiological signals, are widely applied in emotion recognition and show promising performance. However, emotion recognition relying on ET parameters still faces several challenges: (1) insufficient extraction of temporal dynamic information from the ET parameters; (2) a lack of sophisticated features with strong emotional specificity, which restricts the model's robustness and individual generalization capability. To address these issues, we propose a novel Temporal Capsule Feature Network (TCFN) for ET parameter-based emotion recognition. The network incorporates a Window Feature Module to extract Eye Movement temporal dynamic information and a specialized Capsule Network Module to mine complementary and collaborative relationships among features. The MLP Classification Module realizes feature-to-category conversion, and a Dual-Loss Mechanism is integrated to optimize overall performance. Experimental results demonstrate the superiority of the proposed model: the average accuracy reaches 83.27% for Arousal and 89.94% for Valence (three-class tasks) on the eSEE-d dataset, and the accuracy rate of four-category across-session emotion recognition is 63.85% on the SEED-IV dataset.