A Lightweight Attention-based Deep Network for Multi-View Facial Expression Recognition

Conceptos Básicos

Introducing LANMSFF, a lightweight attention-based deep network incorporating multi-scale feature fusion, to address challenges in facial expression recognition.

Resumen

The article introduces the LANMSFF model for facial expression recognition. It addresses challenges of high computational complexity and multi-view head poses. The model incorporates MassAtt and PWFS blocks to enhance feature selection and fusion. Experimental results show robustness and comparable accuracy rates on various datasets.

I. Introduction

• Facial expressions are universal indicators of emotions.
• Deep learning models have shown robustness in recognizing facial expressions.

II. Proposed Method: LANMSFF

• Lightweight FCN model with MassAtt and PWFS blocks.
• Utilizes attention mechanisms and multi-scale features for improved recognition.

III. Experiments & Results

• Achieved accuracy rates of 90.77% on KDEF, 70.44% on FER-2013, and 86.96% on FERPlus datasets.
• Robustness demonstrated against pose variation in multi-view scenarios.

IV. Conclusion & Future Work

• LANMSFF shows promise in addressing challenges of facial expression recognition.
• Future research aims to incorporate dynamic datasets and pose estimation tasks.

Estadísticas

"Our proposed approach achieved results comparable to state-of-the-art methods in terms of parameter counts and robustness to pose variation, with accuracy rates of 90.77% on KDEF, 70.44% on FER-2013, and 86.96% on FERPlus datasets."
"The code for LANMSFF is available at https://github.com/AE-1129/LANMSFF."

Citas

"Deep networks have shown more robustness and effectiveness compared to traditional approaches."
"Utilizing all features from diverse perspectives without considering their importance may negatively impact recognition accuracy."

Ideas clave extraídas de

A Lightweight Attention-based Deep Network via Multi-Scale Feature Fusion for Multi-View Facial Expression Recognition

by Ali Ezati,Mo... a las arxiv.org 03-22-2024

https://arxiv.org/pdf/2403.14318.pdf

Consultas más profundas

How can the LANMSFF model be adapted for real-time applications?

The LANMSFF model can be adapted for real-time applications by optimizing its architecture and implementation. To ensure real-time performance, the model can be streamlined by further reducing computational complexity through techniques like quantization, pruning, or using more efficient layers such as depthwise separable convolutions. Additionally, hardware acceleration with specialized chips like GPUs or TPUs can speed up inference time. Implementing the model on edge devices or cloud-based services with low latency communication can also enhance its real-time capabilities.

What are the potential ethical implications of using facial expression recognition technology?

Facial expression recognition technology raises several ethical concerns related to privacy, consent, bias, and surveillance. One major concern is the potential misuse of this technology for intrusive surveillance without individuals' consent, leading to violations of privacy rights. There is also a risk of algorithmic bias where certain demographics may be disproportionately affected due to inaccuracies in recognizing expressions from diverse populations. Furthermore, there are concerns about data security and the possibility of sensitive facial data being compromised or misused.

How might cultural differences impact the effectiveness of the LANMSFF model?

Cultural differences can impact the effectiveness of facial expression recognition models like LANMSFF due to variations in how emotions are expressed and interpreted across different cultures. Cultural norms influence facial expressions and gestures differently around the world, leading to discrepancies in labeling emotions accurately. The training data used for models like LANMSFF may not adequately represent these cultural nuances, resulting in reduced accuracy when applied to diverse populations. Adapting the model to account for cultural diversity through inclusive datasets and cross-cultural validation could help mitigate these challenges.