scholarly journals Improving Facial Emotion Recognition with Image processing and Deep Learning

2021 ◽  
Author(s):  
Ksheeraj Sai Vepuri
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Emotion Recognition ◽  
Facial Emotion Recognition ◽  
Facial Emotion

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 Real Time Facial Emotion Recognition based on Image Processing and Machine Learning

2016 ◽  
Vol 139 (11) ◽  
pp. 16-19 ◽  
Author(s):  
Rituparna Halder ◽  
Sushmit Sengupta ◽  
Arnab Pal ◽  
Sudipta Ghosh ◽  
Debashish Kundu
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Emotion Recognition ◽  
Real Time ◽  
Facial Emotion Recognition ◽  
Facial Emotion

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

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