A Unified Multi-Branch Vision Transformer for Facial Expression Recognition and Mask Wearing Classification

Temel Kavramlar

A unified multi-branch vision transformer model that extracts shared features for both facial expression recognition and mask wearing classification tasks, using a cross-task fusion phase to effectively leverage the correlation between the two tasks.

Özet

The paper proposes a unified multi-branch vision transformer model for the tasks of facial expression recognition (FER) and mask wearing classification. The model consists of two phases:

Unified Feature Extraction Phase:

The model uses a dual-branch ViT architecture, with a large branch (L-Branch) and a small, complementary branch (S-Branch), to extract multi-scale features.
The outputs of the two branches are fused using a cross-attention module.

Cross-Task Feature Fusion Phase:

Two separate branches, E-Branch for emotion recognition and M-Branch for mask wearing classification, are introduced.
The features from the S-Branch in the first phase are duplicated and fed into both the E-Branch and M-Branch.
The branches are fused using a cross-attention module, and the final classification is performed by aggregating the classification tokens from both branches with the classification token from the L-Branch.

The proposed approach allows for cross-task learning and feature sharing, reducing the overall complexity compared to using separate networks for the two tasks. Extensive experiments demonstrate that the model outperforms or performs on par with state-of-the-art methods on both facial expression recognition and mask wearing classification tasks, while maintaining a relatively low computational cost.

İstatistikler

The model achieves an accuracy of 0.9856 on the CK+ dataset and 0.7702 on the M-CK+ dataset (with masks).
The model achieves an accuracy of 0.7059 on the M-JAFFE dataset, 0.6438 on the M-RAF-DB dataset, and 0.7785 on the M-FER-2013 dataset (all with masks).
The model achieves an accuracy of 0.9793 on the MMD-FMD dataset for mask wearing classification.

Alıntılar

"Our proposed framework reduces the overall complexity compared with using separate networks for both tasks by the simple yet effective cross-task fusion phase."
"Extensive experiments demonstrate that our proposed model performs better than or on par with different state-of-the-art methods on both facial expression recognition and facial mask wearing classification task."

Önemli Bilgiler Şuradan Elde Edildi

Cross-Task Multi-Branch Vision Transformer for Facial Expression and Mask Wearing Classification

by Armando Zhu,... : arxiv.org 04-24-2024

https://arxiv.org/pdf/2404.14606.pdf

Daha Derin Sorular

How can the proposed multi-branch architecture be extended to handle more complex multi-task scenarios, such as incorporating additional vision-based tasks beyond facial expression recognition and mask wearing classification

The proposed multi-branch architecture can be extended to handle more complex multi-task scenarios by incorporating additional vision-based tasks beyond facial expression recognition and mask wearing classification. One way to achieve this is by introducing additional branches dedicated to specific tasks, each sharing the low-level features extracted in the initial phases. For example, tasks like gender recognition, age estimation, or even object detection could be integrated into the model by adding specialized branches with task-specific classification tokens. By expanding the model with more branches, each focusing on a distinct vision task, the architecture can effectively handle a wider range of multi-task scenarios without significantly increasing the computational complexity.

What are the potential limitations of the cross-task fusion approach, and how could it be further improved to better capture the intricate relationships between the tasks

The cross-task fusion approach, while effective in capturing the correlation between facial expression recognition and mask wearing classification tasks, may have limitations in capturing intricate relationships between tasks with varying complexities. One potential limitation is the reliance on predefined fusion mechanisms, such as cross-attention and cross-additive-attention modules, which may not fully capture the nuanced dependencies between tasks. To improve this, the fusion approach could be further enhanced by incorporating adaptive fusion mechanisms that dynamically adjust the information exchange between tasks based on the task-specific features and complexities. Additionally, exploring more advanced fusion techniques, such as graph neural networks or reinforcement learning-based fusion strategies, could provide a more flexible and adaptive way to capture the intricate relationships between tasks in the multi-branch architecture.

Given the increasing importance of facial analysis in human-computer interaction, how could the proposed model be adapted to address emerging challenges, such as recognizing expressions in the presence of other types of occlusions or in diverse cultural contexts

In the context of the increasing importance of facial analysis in human-computer interaction, the proposed model can be adapted to address emerging challenges, such as recognizing expressions in the presence of other types of occlusions or in diverse cultural contexts. To tackle these challenges, the model can be enhanced with robust feature extraction techniques that are resilient to various types of occlusions, such as partial face coverings or accessories. Additionally, incorporating data augmentation strategies that simulate diverse cultural contexts and facial expressions can help improve the model's generalization capabilities. Furthermore, integrating attention mechanisms that focus on specific facial regions, like the eyes or mouth, can enhance the model's ability to recognize expressions even in the presence of occlusions. By adapting the model with these enhancements, it can better handle the complexities of facial analysis in diverse real-world scenarios.