Block Division Convolutional Network with Implicit Deep Features Augmentation for Micro-Expression Recognition

2022 ◽  
pp. 1-1
Author(s):  
Bin Chen ◽  
Kun-Hong Liu ◽  
Yong Xu ◽  
Qing-Qiang Wu ◽  
Jun-Feng Yao
Keyword(s):  
Expression Recognition ◽  
Convolutional Network ◽  
Micro Expression

Deep Convolutional Neural Network with Optical Flow for Facial Micro-Expression Recognition

2019 ◽  
Vol 29 (01) ◽  
pp. 2050006 ◽  
Author(s):  
Qiuyu Li ◽  
Jun Yu ◽  
Toru Kurihara ◽  
Haiyan Zhang ◽  
Shu Zhan
Keyword(s):  
Neural Network ◽  
Optical Flow ◽  
Video Clip ◽  
Support Vector ◽  
Expression Recognition ◽  
Convolutional Network ◽  
Facial Landmarks ◽  
Flow Features ◽  
Optical Flow Features ◽  
Micro Expression

Micro-expression is a kind of brief facial movements which could not be controlled by the nervous system. Micro-expression indicates that a person is hiding his true emotion consciously. Micro-expression recognition has various potential applications in public security and clinical medicine. Researches are focused on the automatic micro-expression recognition, because it is hard to recognize the micro-expression by people themselves. This research proposed a novel algorithm for automatic micro-expression recognition which combined a deep multi-task convolutional network for detecting the facial landmarks and a fused deep convolutional network for estimating the optical flow features of the micro-expression. First, the deep multi-task convolutional network is employed to detect facial landmarks with the manifold-related tasks for dividing the facial region. Furthermore, a fused convolutional network is applied for extracting the optical flow features from the facial regions which contain the muscle changes when the micro-expression appears. Because each video clip has many frames, the original optical flow features of the whole video clip will have high number of dimensions and redundant information. This research revises the optical flow features for reducing the redundant dimensions. Finally, a revised optical flow feature is applied for refining the information of the features and a support vector machine classifier is adopted for recognizing the micro-expression. The main contribution of work is combining the deep multi-task learning neural network and the fusion optical flow network for micro-expression recognition and revising the optical flow features for reducing the redundant dimensions. The results of experiments on two spontaneous micro-expression databases prove that our method achieved competitive performance in micro-expression recognition.

scholarly journals Deep-Emotion: Facial Expression Recognition Using Attentional Convolutional Network

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3046
Author(s):  
Shervin Minaee ◽  
Mehdi Minaei ◽  
Amirali Abdolrashidi
Keyword(s):  
Deep Learning ◽  
Facial Expression ◽  
Facial Expression Recognition ◽  
Expression Recognition ◽  
Visualization Technique ◽  
Convolutional Network ◽  
The Past ◽  
Multiple Datasets ◽  
The Face ◽  
Traditional Approaches

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.

scholarly journals Facial Expression Recognition Based on Multi-Features Cooperative Deep Convolutional Network

2021 ◽  
Vol 11 (4) ◽  
pp. 1428
Author(s):  
Haopeng Wu ◽  
Zhiying Lu ◽  
Jianfeng Zhang ◽  
Xin Li ◽  
Mingyue Zhao ◽  
...  
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Facial Expression Recognition ◽  
Video Data ◽  
Expression Recognition ◽  
Convolutional Network ◽  
Facial Movements ◽  
The Face ◽  
Deep Convolutional Network ◽  
Selection Of

This paper addresses the problem of Facial Expression Recognition (FER), focusing on unobvious facial movements. Traditional methods often cause overfitting problems or incomplete information due to insufficient data and manual selection of features. Instead, our proposed network, which is called the Multi-features Cooperative Deep Convolutional Network (MC-DCN), maintains focus on the overall feature of the face and the trend of key parts. The processing of video data is the first stage. The method of ensemble of regression trees (ERT) is used to obtain the overall contour of the face. Then, the attention model is used to pick up the parts of face that are more susceptible to expressions. Under the combined effect of these two methods, the image which can be called a local feature map is obtained. After that, the video data are sent to MC-DCN, containing parallel sub-networks. While the overall spatiotemporal characteristics of facial expressions are obtained through the sequence of images, the selection of keys parts can better learn the changes in facial expressions brought about by subtle facial movements. By combining local features and global features, the proposed method can acquire more information, leading to better performance. The experimental results show that MC-DCN can achieve recognition rates of 95%, 78.6% and 78.3% on the three datasets SAVEE, MMI, and edited GEMEP, respectively.

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