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.

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 Hybrid Attention Cascade Network for Facial Expression Recognition

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2003 ◽  
Author(s):  
Xiaoliang Zhu ◽  
Shihao Ye ◽  
Liang Zhao ◽  
Zhicheng Dai
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Facial Expression Recognition ◽  
Expression Recognition ◽  
Spatial Features ◽  
Face Images ◽  
Temporal Features ◽  
The Face ◽  
In The Wild ◽  
Fusion Features

As a sub-challenge of EmotiW (the Emotion Recognition in the Wild challenge), how to improve performance on the AFEW (Acted Facial Expressions in the wild) dataset is a popular benchmark for emotion recognition tasks with various constraints, including uneven illumination, head deflection, and facial posture. In this paper, we propose a convenient facial expression recognition cascade network comprising spatial feature extraction, hybrid attention, and temporal feature extraction. First, in a video sequence, faces in each frame are detected, and the corresponding face ROI (range of interest) is extracted to obtain the face images. Then, the face images in each frame are aligned based on the position information of the facial feature points in the images. Second, the aligned face images are input to the residual neural network to extract the spatial features of facial expressions corresponding to the face images. The spatial features are input to the hybrid attention module to obtain the fusion features of facial expressions. Finally, the fusion features are input in the gate control loop unit to extract the temporal features of facial expressions. The temporal features are input to the fully connected layer to classify and recognize facial expressions. Experiments using the CK+ (the extended Cohn Kanade), Oulu-CASIA (Institute of Automation, Chinese Academy of Sciences) and AFEW datasets obtained recognition accuracy rates of 98.46%, 87.31%, and 53.44%, respectively. This demonstrated that the proposed method achieves not only competitive performance comparable to state-of-the-art methods but also greater than 2% performance improvement on the AFEW dataset, proving the significant outperformance of facial expression recognition in the natural environment.

scholarly journals Facial Expression Recognition Based on Auxiliary Models

Algorithms ◽  
2019 ◽  
Vol 12 (11) ◽  
pp. 227 ◽  
Author(s):  
Yingying Wang ◽  
Yibin Li ◽  
Yong Song ◽  
Xuewen Rong
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Facial Expression Recognition ◽  
State Of The Art ◽  
Face Image ◽  
Great Success ◽  
Expression Recognition ◽  
Input Information ◽  
The Face ◽  
Feature Information

In recent years, with the development of artificial intelligence and human–computer interaction, more attention has been paid to the recognition and analysis of facial expressions. Despite much great success, there are a lot of unsatisfying problems, because facial expressions are subtle and complex. Hence, facial expression recognition is still a challenging problem. In most papers, the entire face image is often chosen as the input information. In our daily life, people can perceive other’s current emotions only by several facial components (such as eye, mouth and nose), and other areas of the face (such as hair, skin tone, ears, etc.) play a smaller role in determining one’s emotion. If the entire face image is used as the only input information, the system will produce some unnecessary information and miss some important information in the process of feature extraction. To solve the above problem, this paper proposes a method that combines multiple sub-regions and the entire face image by weighting, which can capture more important feature information that is conducive to improving the recognition accuracy. Our proposed method was evaluated based on four well-known publicly available facial expression databases: JAFFE, CK+, FER2013 and SFEW. The new method showed better performance than most state-of-the-art methods.

scholarly journals Self-Difference Convolutional Neural Network for Facial Expression Recognition

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2250
Author(s):  
Leyuan Liu ◽  
Rubin Jiang ◽  
Jiao Huo ◽  
Jingying Chen
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Facial Expression Recognition ◽  
Low Cost ◽  
Feature Space ◽  
Expression Recognition ◽  
Generative Adversarial Network ◽  
Convolutional Network ◽  
Online Processing ◽  
Adversarial Network

Facial expression recognition (FER) is a challenging problem due to the intra-class variation caused by subject identities. In this paper, a self-difference convolutional network (SD-CNN) is proposed to address the intra-class variation issue in FER. First, the SD-CNN uses a conditional generative adversarial network to generate the six typical facial expressions for the same subject in the testing image. Second, six compact and light-weighted difference-based CNNs, called DiffNets, are designed for classifying facial expressions. Each DiffNet extracts a pair of deep features from the testing image and one of the six synthesized expression images, and compares the difference between the deep feature pair. In this way, any potential facial expression in the testing image has an opportunity to be compared with the synthesized “Self”—an image of the same subject with the same facial expression as the testing image. As most of the self-difference features of the images with the same facial expression gather tightly in the feature space, the intra-class variation issue is significantly alleviated. The proposed SD-CNN is extensively evaluated on two widely-used facial expression datasets: CK+ and Oulu-CASIA. Experimental results demonstrate that the SD-CNN achieves state-of-the-art performance with accuracies of 99.7% on CK+ and 91.3% on Oulu-CASIA, respectively. Moreover, the model size of the online processing part of the SD-CNN is only 9.54 MB (1.59 MB ×6), which enables the SD-CNN to run on low-cost hardware.

scholarly journals EXPLORING ANALYTICAL AND HOLISTIC PROCESSING IN FACIAL EXPRESSION RECOGNITION

2020 ◽  
pp. 103-140
Author(s):  
Yakov A. Bondarenko ◽  
Galina Ya. Menshikova
Keyword(s):  
Facial Expression ◽  
Multidimensional Scaling ◽  
Facial Expressions ◽  
Facial Expression Recognition ◽  
Spatial Configuration ◽  
Expression Recognition ◽  
Recognition Of Facial Expressions ◽  
Relative Contribution ◽  
The Face ◽  
Subjective Space

Background. The study explores two main processes of perception of facial expression: analytical (perception based on individual facial features) and holistic (holistic and non-additive perception of all features). The relative contribution of each process to facial expression recognition is still an open question. Objective. To identify the role of holistic and analytical mechanisms in the process of facial expression recognition. Methods. A method was developed and tested for studying analytical and holistic processes in the task of evaluating subjective differences of expressions, using composite and inverted facial images. A distinctive feature of the work is the use of a multidimensional scaling method, by which a judgment of the contribution of holistic and analytical processes to the perception of facial expressions is based on the analysis of the subjective space of the similarity of expressions obtained when presenting upright and inverted faces. Results. It was shown, first, that when perceiving upright faces, a characteristic clustering of expressions is observed in the subjective space of similarities of expression, which we interpret as a predominance of holistic processes; second, by inversion of the face, there is a change in the spatial configuration of expressions that may reflect a strengthening of analytical processes; in general, the method of multidimensional scaling has proven its effectiveness in solving the problem of the relation between holistic and analytical processes in recognition of facial expressions. Conclusion. The analysis of subjective spaces of the similarity of emotional faces is productive for the study of the ratio of analytical and holistic processes in the recognition of facial expressions.

scholarly journals Facial Expression Recognition Complications with the Stages of Face Detection and Recognition

2019 ◽  
Vol 8 (2) ◽  
pp. 2728-2740 ◽  
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Face Detection ◽  
Facial Expression Recognition ◽  
Feature Recognition ◽  
Expression Recognition ◽  
Facial Movements ◽  
Face Detection And Recognition ◽  
Facial Changes ◽  
Facial Component

Facial expressions are the facial changes in light of a man's interior enthusiastic moods, aims, or social interchanges which are investigated by computer frameworks that endeavor to consequently examine and perceive facial movements and facial component changes from visual data. Now and again the facial expression recognition has been mistaken for feeling examination in the computer vision space prompts uncouth backings of acknowledgment process such as face detection, feature recognition and expression recognition in that way bringing about the issues of identifying impediments, enlightenments, posture varieties, acknowledgment, decrease in dimensionality, and so forth. Notwithstanding that, an appropriate computation and forecast of exact outcomes additionally enhances the execution of the facial Expression recognition. Henceforth, a detailed study was required about the strategies and systems utilized for unraveling the issues of facial expressions during the time of face detection, feature recognition and expression recognition. So thepaper displayed different current strategies and afterward basically considered the effort by the different researchers in the area of Facial Expression Recognition.

scholarly journals Improved Facial Expression Recognition Based on DWT Feature for Deep CNN

Electronics ◽  
2019 ◽  
Vol 8 (3) ◽  
pp. 324 ◽  
Author(s):  
Ridha Bendjillali ◽  
Mohammed Beladgham ◽  
Khaled Merit ◽  
Abdelmalik Taleb-Ahmed
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Facial Expression Recognition ◽  
Histogram Equalization ◽  
Detection Algorithm ◽  
Discrete Wavelet ◽  
Expression Recognition ◽  
Facial Image ◽  
Illumination Changes ◽  
The Face

Facial expression recognition (FER) has become one of the most important fields of research in pattern recognition. In this paper, we propose a method for the identification of facial expressions of people through their emotions. Being robust against illumination changes, this method combines four steps: Viola–Jones face detection algorithm, facial image enhancement using contrast limited adaptive histogram equalization (CLAHE) algorithm, the discrete wavelet transform (DWT), and deep convolutional neural network (CNN). We have used Viola–Jones to locate the face and facial parts; the facial image is enhanced using CLAHE; then facial features extraction is done using DWT; and finally, the extracted features are used directly to train the CNN network, for the purpose of classifying the facial expressions. Our experimental work was performed on the CK+ database and JAFFE face database. The results obtained using this network were 96.46% and 98.43%, respectively.

scholarly journals Facial Expression Recognition of Instructor Using Deep Features and Extreme Learning Machine

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Yusra Khalid Bhatti ◽  
Afshan Jamil ◽  
Nudrat Nida ◽  
Muhammad Haroon Yousaf ◽  
Serestina Viriri ◽  
...  
Keyword(s):  
Facial Expression ◽  
Extreme Learning Machine ◽  
Facial Expressions ◽  
Classroom Environment ◽  
Facial Expression Recognition ◽  
Parameter Tuning ◽  
Expression Recognition ◽  
Fast Learning ◽  
Learning Machine ◽  
The Impact

Classroom communication involves teacher’s behavior and student’s responses. Extensive research has been done on the analysis of student’s facial expressions, but the impact of instructor’s facial expressions is yet an unexplored area of research. Facial expression recognition has the potential to predict the impact of teacher’s emotions in a classroom environment. Intelligent assessment of instructor behavior during lecture delivery not only might improve the learning environment but also could save time and resources utilized in manual assessment strategies. To address the issue of manual assessment, we propose an instructor’s facial expression recognition approach within a classroom using a feedforward learning model. First, the face is detected from the acquired lecture videos and key frames are selected, discarding all the redundant frames for effective high-level feature extraction. Then, deep features are extracted using multiple convolution neural networks along with parameter tuning which are then fed to a classifier. For fast learning and good generalization of the algorithm, a regularized extreme learning machine (RELM) classifier is employed which classifies five different expressions of the instructor within the classroom. Experiments are conducted on a newly created instructor’s facial expression dataset in classroom environments plus three benchmark facial datasets, i.e., Cohn–Kanade, the Japanese Female Facial Expression (JAFFE) dataset, and the Facial Expression Recognition 2013 (FER2013) dataset. Furthermore, the proposed method is compared with state-of-the-art techniques, traditional classifiers, and convolutional neural models. Experimentation results indicate significant performance gain on parameters such as accuracy, F1-score, and recall.

Sign in / Sign up

Export Citation Format

Share Document