ArticleSensors (Basel, Switzerland)2022
Facial Expression Recognition Based on Squeeze Vision Transformer.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- A Hybrid Vision Transformer and EfficientNet-B3 Framework for Facial Expression Recognition.Journal of imaging · 2026Article
- LDR-Net: Landmark-Guided Diverse Regional Representation Learning for Facial Expression Recognition.Biomimetics (Basel, Switzerland) · 2026Article
- FERMam: a lightweight dual-source and multi-scale fusion framework for facial expression recognition.Scientific reports · 2026Article
- KGLA-Net: an efficient facial affective computing framework for psychomotor phenotyping in psychiatry.Frontiers in psychiatry · 2026Article
- ArecaNet: Robust Facial Emotion Recognition via Assembled Residual Enhanced Cross-Attention Networks for Emotion-Aware Human-Computer Interaction.Sensors (Basel, Switzerland) · 2025Article
- Real-Time Face Gesture-Based Robot Control Using GhostNet in a Unity Simulation Environment.Sensors (Basel, Switzerland) · 2025Article
- Application of Multiple Deep Learning Architectures for Emotion Classification Based on Facial Expressions.Sensors (Basel, Switzerland) · 2025Article
- Development and Validation of a Computed Tomography-Based Model for Noninvasive Prediction of the T Stage in Gastric Cancer: Multicenter Retrospective Study.Journal of medical Internet research · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
In recent image classification approaches, a vision transformer (ViT) has shown an excellent performance beyond that of a convolutional neural network. A ViT achieves a high classification for natural images because it properly preserves the global image features. Conversely, a ViT still has many limitations in facial expression recognition (FER), which requires the detection of subtle changes in expression, because it can lose the local features of the image. Therefore, in this paper, we propose Squeeze ViT, a method for reducing the computational complexity by reducing the number of feature dimensions while increasing the FER performance by concurrently combining global and local features. To measure the FER performance of Squeeze ViT, experiments were conducted on lab-controlled FER datasets and a wild FER dataset. Through comparative experiments with previous state-of-the-art approaches, we proved that the proposed method achieves an excellent performance on both types of datasets.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.