Journal of Applied Science and Engineering

Published by Tamkang University Press

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An Efficient Multi-view Transformer for Emotion Recognition of University Students

 2026

 Volume 32

HongXin1, and Lanqiang Cong2

1Weifang Vocational College, Weifang 262737 China

2Shandong Vocational College of Information Technology, Weifang 261061 China

Received: February 7, 2026
Accepted: April 1, 2026
Publication Date: April 30, 2026

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Accurate emotion recognition for university students is essential for mental health monitoring and engagement analysis, yet the intensive computational cost of standard Vision Transformers hinders their deployment on resource-constrained edge devices. To address this challenge, we propose an Efficient Multi-view Transformer (EFFormer) designed for real-time affective computing in campus environments. EFFormer first employs a Bidirectional Mamba strategy to synthesize unified affective representations from multiple views, effectively capturing complex cross-view correlations with linear complexity. Furthermore, we introduce an instance specific adaptive gating mechanism that dynamically executes patch pruning, attention head activation, and transformer block skipping based on the complexity of each input sample. By jointly optimizing the backbone with a resource-aware loss function and utilizing Gumbel-Softmax reparameterization, EFFormer achieves a superior trade-off between recognition accuracy and inference efficiency. Experimental results demonstrate that our framework significantly reduces computational overhead and latency while maintaining high-fidelity emotional state recognition, providing a practical and robust solution for intelligent emotion monitoring in
university settings.

Keywords: Multi-view Transformer; emotion recognition; efficient and effective inference

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