Multimodal Emotion Recognition from Face, Body Posture, and Text Using a Self-Guided Single-Head Quantum Attention GAN with Snow Ablation Optimization

The rapid advancement of multimodal emotion recognition has created new opportunities for enhancing clinical diagnosis, human-computer interaction, and affective computing; nevertheless, current models tend to be plagued by feature fusion limitations, attention deployment, and optimization, resulting in compromised recognition performance. To counter this shortcoming, the current research proposes Multimodal Emotion Recognition with Face, Body Posture, and Text Using a Self-Single Guided Head Quantum Snow Ablation Generative Adversarial Attention Network (3SGH-QG2AN) that is capable of highly accurate and trustworthy emotion classification. The dataset, “Multimodal Emotion Recognition with Face, Body Posture, and Text”, was built from 146 subjects gathered at the Shandong Mental Health. The 3SGH-QG2AN proposed here combines a Self-Guided Quantum Generative Adversarial Network (SG-QGAN) with Single-Head Self-Attention (SHSA), and its hyperparameters are adjusted by the Snow Ablation Optimization (SAO) algorithm to facilitate improved convergence stability and classification robustness. Experimental evidence shows that the 3SGH-QG2AN proposed in this work attains an impressive recognition accuracy.