An efficient deep learning technique for facial emotion recognition

2021 ◽  
Author(s):  
Asad Khattak ◽  
Muhammad Zubair Asghar ◽  
Mushtaq Ali ◽  
Ulfat Batool
Keyword(s):  
Deep Learning ◽  
Emotion Recognition ◽  
Facial Emotion Recognition ◽  
Facial Emotion ◽  
Learning Technique

scholarly journals 3D Facial Emotion Recognition Using Deep Learning Technique

2019 ◽  
Vol 6 (3) ◽  
pp. 64-68
Author(s):  
Pankaj Rao ◽  
Ashish Choudhary ◽  
Vijay Kumar
Keyword(s):  
Deep Learning ◽  
Emotion Recognition ◽  
Facial Emotion Recognition ◽  
Facial Emotion ◽  
Learning Technique

Deep Learning Model for Facial Emotion Recognition

2019 ◽  
pp. 543-558 ◽  
Author(s):  
Ajeet Ram Pathak ◽  
Somesh Bhalsing ◽  
Shivani Desai ◽  
Monica Gandhi ◽  
Pranathi Patwardhan
Keyword(s):  
Deep Learning ◽  
Emotion Recognition ◽  
Learning Model ◽  
Facial Emotion Recognition ◽  
Facial Emotion ◽  
Deep Learning Model

scholarly journals Efficient Facial Emotion Recognition Model using Deep Convolutional Neural Network and Modified Joint Trilateral Filter

2021 ◽  
Author(s):  
Naveen Kumari ◽  
Rekha Bhatia
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Emotion Recognition ◽  
Deep Convolutional Neural Network ◽  
Facial Emotion Recognition ◽  
Facial Emotion ◽  
Learning Models ◽  
Recognition Model ◽  
Trilateral Filter

Abstract Facial emotion recognition extracts the human emotions from the images and videos. As such, it requires an algorithm to understand and model the relationships between faces and facial expressions, and to recognize human emotions. Recently, deep learning models are extensively utilized enhance the facial emotion recognition rate. However, the deep learning models suffer from the overfitting issue. Moreover, deep learning models perform poorly for images which have poor visibility and noise. Therefore, in this paper, a novel deep learning based facial emotion recognition tool is proposed. Initially, a joint trilateral filter is applied to the obtained dataset to remove the noise. Thereafter, contrast-limited adaptive histogram equalization (CLAHE) is applied to the filtered images to improve the visibility of images. Finally, a deep convolutional neural network is trained. Nadam optimizer is also utilized to optimize the cost function of deep convolutional neural networks. Experiments are achieved by using the benchmark dataset and competitive human emotion recognition models. Comparative analysis demonstrates that the proposed facial emotion recognition model performs considerably better compared to the competitive models.

scholarly journals Facial Emotion Recognition using Deep Learning

2019 ◽  
Vol 8 (4) ◽  
pp. 10061-10064
Keyword(s):  
Deep Learning ◽  
Emotion Recognition ◽  
Facial Emotion Recognition ◽  
Facial Emotion ◽  
Human Face ◽  
Face Emotion Recognition

Face Emotion Recognition (FER), the human face assumes a significant job in programmed acknowledgment of feelings in the field of recognizing human feelings and the cooperation among humans and PC for some genuine applications. The greater part of the revealed facial feeling acknowledgment frameworks aren't completely viewed as subject free unique highlights thus they are not hearty enough for reality. The feelings are successfully variable happenings that are evoked because of affecting power. In this way, all things considered, applications, recognition of feeling is an extremely testing assignment

scholarly journals A Review on Facial Emotion Recognition Using Machine and Deep Learning Algorithm

2020 ◽  
pp. 09-13
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Deep Learning ◽  
Emotion Recognition ◽  
Facial Expressions ◽  
Learning Algorithm ◽  
Facial Feature ◽  
Facial Emotion Recognition ◽  
Facial Emotion ◽  
Expression Recognition

Facial emotions are the changes in facial expressions about a person’s inner excited tempers, objectives, or social exchanges which are scrutinized with the aid of computer structures that attempt to subsequently inspect and identify the facial feature and movement variations from visual data. Facial emotion recognition (FER) is a noteworthy area in the arena of computer vision and artificial intelligence due to its significant commercial and academic potential. FER has become a widespread concept of deep learning and offers more fields for application in our day-to-day life. Facial expression recognition (FER) has gathered widespread consideration recently as facial expressions are thought of as the fastest medium for communicating any of any sort of information. Recognizing facial expressions provides an improved understanding of a person’s thoughts or views. With the latest improvement in computer vision and machine learning, it is plausible to identify emotions from images. Analyzing them with the presently emerging deep learning methods enhance the accuracy rate tremendously as compared to the traditional contemporary systems. This paper emphases the review of a few of the machine learning, deep learning, and transfer learning techniques used by several researchers that flagged the means to advance the classification accurateness of the FEM.

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