Emotic Masked Autoencoder with Attention Fusion for Facial Expression Recognition

• URL: http://arxiv.org/abs/2403.13039v3
• Date: Sun, 12 May 2024 10:33:42 GMT
• Title: Emotic Masked Autoencoder with Attention Fusion for Facial Expression Recognition
• Authors: Bach Nguyen-Xuan, Thien Nguyen-Hoang, Thanh-Huy Nguyen, Nhu Tai-Do,
• Abstract summary: This paper presents an innovative approach integrating the MAE-Face self-supervised learning (SSL) method and multi-view Fusion Attention mechanism for expression classification. We suggest easy-to-implement and no-training frameworks aimed at highlighting key facial features to determine if such features can serve as guides for the model. The efficacy of this method is validated by improvements in model performance on the Aff-wild2 dataset.
• Score: 1.4374467687356276
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Facial Expression Recognition (FER) is a critical task within computer vision with diverse applications across various domains. Addressing the challenge of limited FER datasets, which hampers the generalization capability of expression recognition models, is imperative for enhancing performance. Our paper presents an innovative approach integrating the MAE-Face self-supervised learning (SSL) method and multi-view Fusion Attention mechanism for expression classification, particularly showcased in the 6th Affective Behavior Analysis in-the-wild (ABAW) competition. By utilizing low-level feature information from the ipsilateral view (auxiliary view) before learning the high-level feature that emphasizes the shift in the human facial expression, our work seeks to provide a straightforward yet innovative way to improve the examined view (main view). We also suggest easy-to-implement and no-training frameworks aimed at highlighting key facial features to determine if such features can serve as guides for the model, focusing on pivotal local elements. The efficacy of this method is validated by improvements in model performance on the Aff-wild2 dataset, as observed in both training and validation contexts.

Related papers

• Exploring Facial Expression Recognition through Semi-Supervised Pretraining and Temporal Modeling [8.809586885539002]
  This paper presents our approach for the upcoming 6th Affective Behavior Analysis in-the-Wild (ABAW) competition. In the 6th ABAW competition, our method achieved outstanding results on the official validation set.
  arXiv  Detail & Related papers  (2024-03-18T16:36:54Z)
• Faceptor: A Generalist Model for Face Perception [52.8066001012464]
  Faceptor is proposed to adopt a well-designed single-encoder dual-decoder architecture. Layer-Attention into Faceptor enables the model to adaptively select features from optimal layers to perform the desired tasks. Our training framework can also be applied to auxiliary supervised learning, significantly improving performance in data-sparse tasks such as age estimation and expression recognition.
  arXiv  Detail & Related papers  (2024-03-14T15:42:31Z)
• Exploring Large-scale Unlabeled Faces to Enhance Facial Expression Recognition [12.677143408225167]
  We propose a semi-supervised learning framework that utilizes unlabeled face data to train expression recognition models effectively. Our method uses a dynamic threshold module that can adaptively adjust the confidence threshold to fully utilize the face recognition data. In the ABAW5 EXPR task, our method achieved excellent results on the official validation set.
  arXiv  Detail & Related papers  (2023-03-15T13:43:06Z)
• Learning Diversified Feature Representations for Facial Expression Recognition in the Wild [97.14064057840089]
  We propose a mechanism to diversify the features extracted by CNN layers of state-of-the-art facial expression recognition architectures. Experimental results on three well-known facial expression recognition in-the-wild datasets, AffectNet, FER+, and RAF-DB, show the effectiveness of our method.
  arXiv  Detail & Related papers  (2022-10-17T19:25:28Z)
• Cluster-level pseudo-labelling for source-free cross-domain facial expression recognition [94.56304526014875]
  We propose the first Source-Free Unsupervised Domain Adaptation (SFUDA) method for Facial Expression Recognition (FER) Our method exploits self-supervised pretraining to learn good feature representations from the target data. We validate the effectiveness of our method in four adaptation setups, proving that it consistently outperforms existing SFUDA methods when applied to FER.
  arXiv  Detail & Related papers  (2022-10-11T08:24:50Z)
• CIAO! A Contrastive Adaptation Mechanism for Non-Universal Facial Expression Recognition [80.07590100872548]
  We propose Contrastive Inhibitory Adaptati On (CIAO), a mechanism that adapts the last layer of facial encoders to depict specific affective characteristics on different datasets. CIAO presents an improvement in facial expression recognition performance over six different datasets with very unique affective representations.
  arXiv  Detail & Related papers  (2022-08-10T15:46:05Z)
• Deep Collaborative Multi-Modal Learning for Unsupervised Kinship Estimation [53.62256887837659]
  Kinship verification is a long-standing research challenge in computer vision. We propose a novel deep collaborative multi-modal learning (DCML) to integrate the underlying information presented in facial properties. Our DCML method is always superior to some state-of-the-art kinship verification methods.
  arXiv  Detail & Related papers  (2021-09-07T01:34:51Z)
• Self-supervised Contrastive Learning of Multi-view Facial Expressions [9.949781365631557]
  Facial expression recognition (FER) has emerged as an important component of human-computer interaction systems. We propose Contrastive Learning of Multi-view facial Expressions (CL-MEx) to exploit facial images captured simultaneously from different angles towards FER.
  arXiv  Detail & Related papers  (2021-08-15T11:23:34Z)
• Heterogeneous Contrastive Learning: Encoding Spatial Information for Compact Visual Representations [183.03278932562438]
  This paper presents an effective approach that adds spatial information to the encoding stage to alleviate the learning inconsistency between the contrastive objective and strong data augmentation operations. We show that our approach achieves higher efficiency in visual representations and thus delivers a key message to inspire the future research of self-supervised visual representation learning.
  arXiv  Detail & Related papers  (2020-11-19T16:26:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.