Deep-Emotion: Facial Expression Recognition Using Attentional Convolutional Network.
TLDR
In this paper, a deep learning approach based on attentional convolutional network that is able to focus on important parts of the face and achieves significant improvement over previous models on multiple datasets, including FER-2013, CK+, FERG, and JAFFE.Abstract:
Facial expression recognition has been an active area of research over the past few decades, and it is still challenging due to the high intra-class variation. Traditional approaches for this problem rely on hand-crafted features such as SIFT, HOG, and LBP, followed by a classifier trained on a database of images or videos. Most of these works perform reasonably well on datasets of images captured in a controlled condition but fail to perform as well on more challenging datasets with more image variation and partial faces. In recent years, several works proposed an end-to-end framework for facial expression recognition using deep learning models. Despite the better performance of these works, there are still much room for improvement. In this work, we propose a deep learning approach based on attentional convolutional network that is able to focus on important parts of the face and achieves significant improvement over previous models on multiple datasets, including FER-2013, CK+, FERG, and JAFFE. We also use a visualization technique that is able to find important facial regions to detect different emotions based on the classifier’s output. Through experimental results, we show that different emotions are sensitive to different parts of the face.read more
Citations
More filters
Journal ArticleDOI
Significance of sensors for industry 4.0: Roles, capabilities, and applications
TL;DR: Sensors are vital components of Industry 4.0, allowing several transitions such as changes in positions, length, height, external and dislocations in industrial production facilities to be detected, measured, analysed, and processed.
Journal ArticleDOI
Facial expression recognition with grid-wise attention and visual transformer
TL;DR: A novel FER framework with two attention mechanisms for CNN-based models are introduced, and these two Attention mechanisms are used for the low-level feature learning the high-level semantic representation and the global representation, respectively.
Facial emotion recognition based on Textural pattern and Convolutional Neural Network
TL;DR: In this article, a new approach to detect basic facial expressions from textural images using deep learning model Convolution neural network (CNN) was proposed by using Local Binary Pattern (LBP) method and trained the CNN model with the LBPimages.
Journal ArticleDOI
Multimodal Emotion Recognition on RAVDESS Dataset Using Transfer Learning.
Cristina Luna-Jiménez,David Griol,Zoraida Callejas,Ricardo Kleinlein,Juan Manuel Montero,Fernando Fernández-Martínez +5 more
TL;DR: In this article, a multimodal emotion recognition system that relies on speech and facial information was proposed, which achieved 80.08% accuracy on the RAVDESS dataset on a subject-wise 5-CV evaluation, classifying eight emotions.
Journal ArticleDOI
Local Multi-Head Channel Self-Attention for Facial Expression Recognition
TL;DR: LHC as discussed by the authors is a self-attention module that can be easily integrated into virtually every convolutional neural network, and that is specifically designed for computer vision, with a specific focus on facial expression recognition.
References
More filters
Proceedings ArticleDOI
Deep Residual Learning for Image Recognition
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
Journal ArticleDOI
Generative Adversarial Nets
Ian Goodfellow,Jean Pouget-Abadie,Mehdi Mirza,Bing Xu,David Warde-Farley,Sherjil Ozair,Aaron Courville,Yoshua Bengio +7 more
TL;DR: A new framework for estimating generative models via an adversarial process, in which two models are simultaneously train: a generative model G that captures the data distribution and a discriminative model D that estimates the probability that a sample came from the training data rather than G.
Proceedings ArticleDOI
Fully convolutional networks for semantic segmentation
TL;DR: The key insight is to build “fully convolutional” networks that take input of arbitrary size and produce correspondingly-sized output with efficient inference and learning.
Book ChapterDOI
Visualizing and Understanding Convolutional Networks
Matthew D. Zeiler,Rob Fergus +1 more
TL;DR: A novel visualization technique is introduced that gives insight into the function of intermediate feature layers and the operation of the classifier in large Convolutional Network models, used in a diagnostic role to find model architectures that outperform Krizhevsky et al on the ImageNet classification benchmark.
Proceedings Article
Mask R-CNN
TL;DR: This work presents a conceptually simple, flexible, and general framework for object instance segmentation that outperforms all existing, single-model entries on every task, including the COCO 2016 challenge winners.