scholarly journals A Compact Deep Learning Model for Robust Facial Expression Recognition

2019 ◽  
Vol 8 (6) ◽  
pp. 2956-2960
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Facial Expression Recognition ◽  
Input Image ◽  
Image Resolution ◽  
Training Data ◽  
Expression Recognition ◽  
Human Beings ◽  
The Public ◽  
Low Intensity

In this paper we are proposing a compact CNN model for facial expression recognition. Expression recognition on the low quality images are much more challenging and interesting due to the presence of low-intensity expressions. These low intensity expressions are difficult to distinguish with insufficient image resolution. Data collection for FER is expensive and time-consuming. Researches indicates the fact that downloaded images from the Internet is very useful to model and train expression recognition problem. We use extra datasets to improve the training of facial expression recognition, each representing specific data source. Moreover, to prevent subjective annotation, each dataset is labeled with different approaches to ensure annotation qualities. Recognizing the precise and exact expression from a variety of expressions of different people is a huge problem. To solve this problem, we proposed an Emotion Detection Model to extract emotions from the given input image. This work mainly focuses on the psychological approach of color circle-emotion relation[1] to find the accurate emotion from the input image. Initially the whole image is preprocessed and pixel by pixel data is studied. And the combinations of the circles based on combined data will result in a new color. This resulted color will be directly correlated to a particular emotion. Based on the psychological aspects the output will be of reasonable accuracy. The major application of our work is to predict a person’s emotion based on his face images or video frames This can even be applied for evaluating the public opinion relating to a particular movie, form the video reaction posts on social Medias. One of the diverse applications of our system is to understand the students learning from their emotions. Human beings shows their emotional states and intentions through facial expressions.. Facial expressions are powerful and natural methods that emphasize the emotional status of humans .The approach used in this work successfully exploits temporal information and it improves the accuracies on the public benchmarking databases. The basic facial expressions are happiness, fear, anger, disgust sadness, and surprise[2]. Contempt was subsequently added as one of the basic emotions. Having sufficient well labeled training data with variations of the populations and environments is important for the design of a deep expression recognition system .Behaviors, poses, facial expressions, actions and speech are considered as channels, which convey human emotions. Lot of research works are going on in this field to explore the correlation between the above mentioned channels and emotions. This paper highlights on the development of a system which automatically recognizes the

Incremental Learning-Based Facial Expression Classification System Using a Novel Multinomial Classifier

2017 ◽  
Vol 32 (04) ◽  
pp. 1856004 ◽  
Author(s):  
M. Sultan Zia ◽  
Majid Hussain ◽  
M. Arfan Jaffar
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Incremental Learning ◽  
Facial Expression Recognition ◽  
Expression Patterns ◽  
Training Session ◽  
Recognition System ◽  
High Accuracy ◽  
Training Data ◽  
Expression Recognition

Facial expressions recognition is a crucial task in pattern recognition and it becomes even crucial when cross-cultural emotions are encountered. Various studies in the past have shown that all the facial expressions are not innate and universal, but many of them are learned and culture-dependent. Extreme facial expression recognition methods employ different datasets for training and later use it for testing and demostrate high accuracy in recognition. Their performances degrade drastically when expression images are taken from different cultures. Moreover, there are many existing facial expression patterns which cannot be generated and used as training data in single training session. A facial expression recognition system can maintain its high accuracy and robustness globally and for a longer period if the system possesses the ability to learn incrementally. We also propose a novel classification algorithm for multinomial classification problems. It is an efficient classifier and can be a good choice for base classifier in real-time applications. We propose a facial expression recognition system that can learn incrementally. We use Local Binary Pattern (LBP) features to represent the expression space. The performance of the system is tested on static images from six different databases containing expressions from various cultures. The experiments using the incremental learning classification demonstrate promising results.

scholarly journals Facial Expression Recognition System Using Facial Characteristic Points And ID3

2014 ◽  
pp. 109-113
Author(s):  
Shubhrata Gupta ◽  
Keshri Verma ◽  
Nazil Perveen
Keyword(s):  
Decision Tree ◽  
Facial Expression ◽  
Facial Expressions ◽  
Facial Expression Recognition ◽  
Recognition System ◽  
Expression Recognition ◽  
Human Beings ◽  
Novel Approach ◽  
Facial Characteristic ◽  
Characteristic Points

Facial expression is one of the most powerful, natural, and abrupt means for human beings which have the knack to communicate emotion and regulate inter-personal behaviour. In this paper we present a novel approach for facial expression detection using decision tree. Facial expression information is mostly concentrate on facial expression information regions, so the mouth, eye and eyebrow regions are segmented from the facial expression images firstly. Using these templates we calculate 30 facial characteristics points (FCP’s). These facial characteristic points describe the position and shape of the above three organs to find diverse parameters which are input to the decision tree for recognizing different facial expressions.

scholarly journals Feature Guided CNN for Baby’s Facial Expression Recognition

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Qing Lin ◽  
Ruili He ◽  
Peihe Jiang
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Facial Expression Recognition ◽  
Recognition Accuracy ◽  
State Of The Art ◽  
Image Database ◽  
Face Image ◽  
Expression Recognition ◽  
Human Beings ◽  
Face Datasets

State-of-the-art facial expression methods outperform human beings, especially, thanks to the success of convolutional neural networks (CNNs). However, most of the existing works focus mainly on analyzing an adult’s face and ignore the important problems: how can we recognize facial expression from a baby’s face image and how difficult is it? In this paper, we first introduce a new face image database, named BabyExp, which contains 12,000 images from babies younger than two years old, and each image is with one of three facial expressions (i.e., happy, sad, and normal). To the best of our knowledge, the proposed dataset is the first baby face dataset for analyzing a baby’s face image, which is complementary to the existing adult face datasets and can shed some light on exploring baby face analysis. We also propose a feature guided CNN method with a new loss function, called distance loss, to optimize interclass distance. In order to facilitate further research, we provide the benchmark of expression recognition on the BabyExp dataset. Experimental results show that the proposed network achieves the recognition accuracy of 87.90% on BabyExp.

scholarly journals Facial Expression Recognition of Nonlinear Facial Variations Using Deep Locality De-Expression Residue Learning in the Wild

Electronics ◽  
2019 ◽  
Vol 8 (12) ◽  
pp. 1487 ◽  
Author(s):  
Asad Ullah ◽  
Jing Wang ◽  
M. Shahid Anwar ◽  
Usman Ahmad ◽  
Uzair Saeed ◽  
...  
Keyword(s):  
Facial Expression ◽  
Real World ◽  
Facial Expression Recognition ◽  
Eye Gaze ◽  
Input Image ◽  
Training Data ◽  
Head Pose Estimation ◽  
Expression Recognition ◽  
Action Unit ◽  
In The Wild

Automatic facial expression recognition is an emerging field. Moreover, the interest has been increased with the transition from laboratory-controlled conditions to in the wild scenarios. Most of the research has been done over nonoccluded faces under the constrained environment, while automatic facial expression is less understood/implemented for partial occlusion in the real world conditions. Apart from that, our research aims to tackle the issues of overfitting (caused by the shortage of adequate training data) and to alleviate the expression-unrelated/intraclass/nonlinear facial variations, such as head pose estimation, eye gaze estimation, intensity and microexpressions. In our research, we control the magnitude of each Action Unit (AU) and combine several of the Action Unit combinations to leverage learning from the generative and discriminative representations for automatic FER. We have also addressed the problem of diversification of expressions from lab controlled to real-world scenarios from our cross-database study and proposed a model for enhancement of the discriminative power of deep features while increasing the interclass scatters, by preserving the locality closeness. Furthermore, facial expression consists of an expressive component as well as neutral component, so we proposed a generative model which is capable of generating neutral expression from an input image using cGAN. The expressive component is filtered and passed to the intermediate layers and the process is called De-expression Residue Learning. The residue in the intermediate/middle layers is very important for learning through expressive components. Finally, we validate the effectiveness of our method (DLP-DeRL) through qualitative and quantitative experimental results using four databases. Our method is more accurate and robust, and outperforms all the existing methods (hand crafted features and deep learning) while dealing the images in the wild.

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 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 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.

scholarly journals Facial Expression Recognition using Residual Convnet with Image Augmentations

2021 ◽  
Vol 14 (2) ◽  
pp. 127-135
Author(s):  
Fadhil Yusuf Rahadika ◽  
Novanto Yudistira ◽  
Yuita Arum Sari
Keyword(s):  
Neural Networks ◽  
Facial Expression ◽  
Test Data ◽  
Facial Expression Recognition ◽  
Optimization Method ◽  
Training Data ◽  
Batch Size ◽  
Online Video ◽  
Expression Recognition ◽  
Validation Data

During the COVID-19 pandemic, many offline activities are turned into online activities via video meetings to prevent the spread of the COVID 19 virus. In the online video meeting, some micro-interactions are missing when compared to direct social interactions. The use of machines to assist facial expression recognition in online video meetings is expected to increase understanding of the interactions among users. Many studies have shown that CNN-based neural networks are quite effective and accurate in image classification. In this study, some open facial expression datasets were used to train CNN-based neural networks with a total number of training data of 342,497 images. This study gets the best results using ResNet-50 architecture with Mish activation function and Accuracy Booster Plus block. This architecture is trained using the Ranger and Gradient Centralization optimization method for 60000 steps with a batch size of 256. The best results from the training result in accuracy of AffectNet validation data of 0.5972, FERPlus validation data of 0.8636, FERPlus test data of 0.8488, and RAF-DB test data of 0.8879. From this study, the proposed method outperformed plain ResNet in all test scenarios without transfer learning, and there is a potential for better performance with the pre-training model. The code is available at https://github.com/yusufrahadika-facial-expressions-essay.

scholarly journals A SYSTEMATIC REVIEW OF METHODS OF EMOTION RECOGNITION BY FACIAL EXPRESSIONS

2021 ◽  
Vol 9 (5) ◽  
pp. 1141-1152
Author(s):  
Muazu Abdulwakil Auma ◽  
Eric Manzi ◽  
Jibril Aminu
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Facial Expression ◽  
Facial Expressions ◽  
Facial Expression Recognition ◽  
Deep Neural Networks ◽  
Scientific Research ◽  
Expression Recognition ◽  
Technical Literature ◽  
Research Results

Facial recognition is integral and essential in todays society, and the recognition of emotions based on facial expressions is already becoming more usual. This paper analytically provides an overview of the databases of video data of facial expressions and several approaches to recognizing emotions by facial expressions by including the three main image analysis stages, which are pre-processing, feature extraction, and classification. The paper presents approaches based on deep learning using deep neural networks and traditional means to recognizing human emotions based on visual facial features. The current results of some existing algorithms are presented. When reviewing scientific and technical literature, the focus was mainly on sources containing theoretical and research information of the methods under consideration and comparing traditional techniques and methods based on deep neural networks supported by experimental research. An analysis of scientific and technical literature describing methods and algorithms for analyzing and recognizing facial expressions and world scientific research results has shown that traditional methods of classifying facial expressions are inferior in speed and accuracy to artificial neural networks. This reviews main contributions provide a general understanding of modern approaches to facial expression recognition, which will allow new researchers to understand the main components and trends in facial expression recognition. A comparison of world scientific research results has shown that the combination of traditional approaches and approaches based on deep neural networks show better classification accuracy. However, the best classification methods are artificial neural networks.

scholarly journals Improving Human–Robot Interaction by Enhancing NAO Robot Awareness of Human Facial Expression

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6438
Author(s):  
Chiara Filippini ◽  
David Perpetuini ◽  
Daniela Cardone ◽  
Arcangelo Merla
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Facial Expression Recognition ◽  
Affective Computing ◽  
Human Robot Interaction ◽  
Human Interaction ◽  
Expression Recognition ◽  
Robot Interaction ◽  
Interaction Field ◽  
Nao Robot

An intriguing challenge in the human–robot interaction field is the prospect of endowing robots with emotional intelligence to make the interaction more genuine, intuitive, and natural. A crucial aspect in achieving this goal is the robot’s capability to infer and interpret human emotions. Thanks to its design and open programming platform, the NAO humanoid robot is one of the most widely used agents for human interaction. As with person-to-person communication, facial expressions are the privileged channel for recognizing the interlocutor’s emotional expressions. Although NAO is equipped with a facial expression recognition module, specific use cases may require additional features and affective computing capabilities that are not currently available. This study proposes a highly accurate convolutional-neural-network-based facial expression recognition model that is able to further enhance the NAO robot’ awareness of human facial expressions and provide the robot with an interlocutor’s arousal level detection capability. Indeed, the model tested during human–robot interactions was 91% and 90% accurate in recognizing happy and sad facial expressions, respectively; 75% accurate in recognizing surprised and scared expressions; and less accurate in recognizing neutral and angry expressions. Finally, the model was successfully integrated into the NAO SDK, thus allowing for high-performing facial expression classification with an inference time of 0.34 ± 0.04 s.

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