Facial Emotion Recognition: State of the Art Performance on FER2013

• URL: http://arxiv.org/abs/2105.03588v1
• Date: Sat, 8 May 2021 04:20:53 GMT
• Title: Facial Emotion Recognition: State of the Art Performance on FER2013
• Authors: Yousif Khaireddin, Zhuofa Chen
• Abstract summary: We achieve the highest single-network classification accuracy on the FER2013 dataset. Our model achieves state-of-the-art single-network accuracy of 73.28 % on FER2013 without using extra training data.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Facial emotion recognition (FER) is significant for human-computer interaction such as clinical practice and behavioral description. Accurate and robust FER by computer models remains challenging due to the heterogeneity of human faces and variations in images such as different facial pose and lighting. Among all techniques for FER, deep learning models, especially Convolutional Neural Networks (CNNs) have shown great potential due to their powerful automatic feature extraction and computational efficiency. In this work, we achieve the highest single-network classification accuracy on the FER2013 dataset. We adopt the VGGNet architecture, rigorously fine-tune its hyperparameters, and experiment with various optimization methods. To our best knowledge, our model achieves state-of-the-art single-network accuracy of 73.28 % on FER2013 without using extra training data.

Related papers

• A Comparative Study of Transfer Learning for Emotion Recognition using CNN and Modified VGG16 Models [0.0]
  We investigate the performance of CNN and Modified VGG16 models for emotion recognition tasks across two datasets: FER2013 and AffectNet. Our findings reveal that both models achieve reasonable performance on the FER2013 dataset, with the Modified VGG16 model demonstrating slightly increased accuracy. When evaluated on the Affect-Net dataset, performance declines for both models, with the Modified VGG16 model continuing to outperform the CNN.
  arXiv  Detail & Related papers  (2024-07-19T17:41:46Z)
• Opinion-Unaware Blind Image Quality Assessment using Multi-Scale Deep Feature Statistics [54.08757792080732]
  We propose integrating deep features from pre-trained visual models with a statistical analysis model to achieve opinion-unaware BIQA (OU-BIQA) Our proposed model exhibits superior consistency with human visual perception compared to state-of-the-art BIQA models.
  arXiv  Detail & Related papers  (2024-05-29T06:09:34Z)
• 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)
• Hybrid Facial Expression Recognition (FER2013) Model for Real-Time Emotion Classification and Prediction [0.0]
  This paper proposes a hybrid model for Facial Expression recognition, which comprises a Deep Convolutional Neural Network (DCNN) and Haar Cascade deep learning architectures. The objective is to classify real-time and digital facial images into one of the seven facial emotion categories considered. The experimental results show a significantly improved classification performance compared to state-of-the-art experiments and research.
  arXiv  Detail & Related papers  (2022-06-19T23:43:41Z)
• Multi-Branch Deep Radial Basis Function Networks for Facial Emotion Recognition [80.35852245488043]
  We propose a CNN based architecture enhanced with multiple branches formed by radial basis function (RBF) units. RBF units capture local patterns shared by similar instances using an intermediate representation. We show it is the incorporation of local information what makes the proposed model competitive.
  arXiv  Detail & Related papers  (2021-09-07T21:05:56Z)
• Facial Expressions Recognition with Convolutional Neural Networks [0.0]
  We will be diving into implementing a system for recognition of facial expressions (FER) by leveraging neural networks. We demonstrate a state-of-the-art single-network-accuracy of 70.10% on the FER2013 dataset without using any additional training data.
  arXiv  Detail & Related papers  (2021-07-19T06:41:00Z)
• Facial expression and attributes recognition based on multi-task learning of lightweight neural networks [9.162936410696409]
  We examine the multi-task training of lightweight convolutional neural networks for face identification and classification of facial attributes. It is shown that it is still necessary to fine-tune these networks in order to predict facial expressions. Several models are presented based on MobileNet, EfficientNet and RexNet architectures.
  arXiv  Detail & Related papers  (2021-03-31T14:21:04Z)
• The FaceChannel: A Fast & Furious Deep Neural Network for Facial Expression Recognition [71.24825724518847]
  Current state-of-the-art models for automatic Facial Expression Recognition (FER) are based on very deep neural networks that are effective but rather expensive to train. We formalize the FaceChannel, a light-weight neural network that has much fewer parameters than common deep neural networks. We demonstrate how our model achieves a comparable, if not better, performance to the current state-of-the-art in FER.
  arXiv  Detail & Related papers  (2020-09-15T09:25:37Z)
• Unsupervised Learning Facial Parameter Regressor for Action Unit Intensity Estimation via Differentiable Renderer [51.926868759681014]
  We present a framework to predict the facial parameters based on a bone-driven face model (BDFM) under different views. The proposed framework consists of a feature extractor, a generator, and a facial parameter regressor.
  arXiv  Detail & Related papers  (2020-08-20T09:49:13Z)
• The FaceChannel: A Light-weight Deep Neural Network for Facial Expression Recognition [71.24825724518847]
  Current state-of-the-art models for automatic FER are based on very deep neural networks that are difficult to train. We formalize the FaceChannel, a light-weight neural network that has much fewer parameters than common deep neural networks. We demonstrate how the FaceChannel achieves a comparable, if not better, performance, as compared to the current state-of-the-art in FER.
  arXiv  Detail & Related papers  (2020-04-17T12:03:14Z)

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.