Improved face transformer model for facial emotion recognition in human-machine collaboration

Sanjeev Roka; Danda B. Rawat

doi:10.1117/12.3014234

7 June 2024 Improved face transformer model for facial emotion recognition in human-machine collaboration

Sanjeev Roka, Danda B. Rawat

Proceedings Volume 13051, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications VI; 130510I (2024) https://doi.org/10.1117/12.3014234
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States

Abstract

With the spread of powerful AI models, human-machine interaction has improved rapidly. In this situation, recognizing facial expressions of human emotion is essential for successful human-machine collaboration/ teaming. While Convolutional Neural Network (CNN) models were widely used for facial emotion classification, Transformer-based models, known for excelling in NLP tasks, have demonstrated superior performance in areas like image classification, semantic segmentation, and object detection. This study investigates the effectiveness of using a transformer based model, the Face-transformer model a powerful tool for facial identification, that will be fine-tuned for the Facial Emotion Recognition (FER) challenge. We aim to modify the face transformer architecture to recognize emotional states from facial photos using the extensive facial emotion recognition datasets, opening the door for more natural and responsive machine interactions. Our initial findings suggest that the Face-Transformer model holds promise for bridging the gap between machine interpretability and human emotions, potentially paving the way for more natural and responsive human-computer interactions.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Sanjeev Roka and Danda B. Rawat "Improved face transformer model for facial emotion recognition in human-machine collaboration", Proc. SPIE 13051, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications VI, 130510I (7 June 2024); https://doi.org/10.1117/12.3014234

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