scholarly journals EMOTION EVALUATION FROM FACIAL EXPRESSION RECOGNITION USING AI TECHNIQUES: A Review

2020 ◽  
Author(s):  
Sonali Singh
Keyword(s):  
Artificial Intelligence ◽  
Facial Expression ◽  
Human Behavior ◽  
Facial Expression Recognition ◽  
Primitive Element ◽  
Image Data ◽  
Expression Recognition ◽  
Human Interactions ◽  
Expression Evaluation ◽  
Human Facial Expression

Facial expression is a primitive element for human interactions. To understand human behavior or mood, it is essential to analyze human facial expression from multidimensional sensitive and feeling image data. Various Artificial Intelligence based techniques are used for facial expression evaluation. In this paper an attempt has been done to Facial expression recognition & emotion evaluation. Previous and recent researches have been investigated to find out the related effective method

scholarly journals EMONET: A Cross Database Progressive Deep Network for Facial Expression Recognition

2020 ◽  
Vol 13 (6) ◽  
pp. 31-41
Author(s):  
Michael Thiruthuvanathan ◽  
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Balachandran Krishnan ◽  
Keyword(s):  
Facial Expression ◽  
Facial Expression Recognition ◽  
Image Data ◽  
Filter Method ◽  
Expression Recognition ◽  
Deep Convolutional Neural Networks ◽  
Improved Performance ◽  
Improved Learning ◽  
Improved Accuracy ◽  
Progressive Learning

Recognizing facial features to detect emotions has always been an interesting topic for research in the field of Computer vision and cognitive emotional analysis. In this research a model to detect and classify emotions is explored, using Deep Convolutional Neural Networks (DCNN). This model intends to classify the primary emotions (Anger, Disgust, Fear, Happy, Sad, Surprise and Neutral) using progressive learning model for a Facial Expression Recognition (FER) System. The proposed model (EmoNet) is developed based on a linear growing-shrinking filter method that shows prominent extraction of robust features for learning and interprets emotional classification for an improved accuracy. EmoNet incorporates Progressive- Resizing (PR) of images to accommodate improved learning traits from emotional datasets by adding more image data for training and Validation which helped in improving the model’s accuracy by 5%. Cross validations were carried out on the model, this enabled the model to be ready for testing on new data. EmoNet results signifies improved performance with respect to accuracy, precision and recall due to the incorporation of progressive learning Framework, Tuning Hyper parameters of the network, Image Augmentation and moderating generalization and Bias on the images. These parameters are compared with the existing models of Emotional analysis with the various datasets that are prominently available for research. The Methods, Image Data and the Fine-tuned model combinedly contributed in achieving 83.6%, 78.4%, 98.1% and 99.5% on FER2013, IMFDB, CK+ and JAFFE respectively. EmoNet has worked on four different datasets and achieved an overall accuracy of 90%.

scholarly journals A Novel Maximum Entropy Markov Model for Human Facial Expression Recognition

PLoS ONE ◽  
2016 ◽  
Vol 11 (9) ◽  
pp. e0162702 ◽  
Author(s):  
Muhammad Hameed Siddiqi ◽  
Md. Golam Rabiul Alam ◽  
Choong Seon Hong ◽  
Adil Mehmood Khan ◽  
Hyunseung Choo
Keyword(s):  
Facial Expression ◽  
Markov Model ◽  
Maximum Entropy ◽  
Facial Expression Recognition ◽  
Expression Recognition ◽  
Human Facial Expression

Facial Expression Recognition with Gender Identification

2020 ◽  
pp. 57-63
Author(s):  
admin admin ◽  
◽  
◽  
◽  
◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Facial Expression ◽  
Facial Expression Recognition ◽  
Expression Recognition ◽  
Human Face ◽  
Gender Identification ◽  
Artificial Intelligence Techniques ◽  
The Mind

The human facial emotions recognition has attracted interest in the field of Artificial Intelligence. The emotions on a human face depicts what’s going on inside the mind. Facial expression recognition is the part of Facial recognition which is gaining more importance and need for it increases tremendously. Though there are methods to identify expressions using machine learning and Artificial Intelligence techniques, this work attempts to use convolution neural networks to recognize expressions and classify the expressions into 6 emotions categories. Various datasets are investigated and explored for training expression recognition models are explained in this paper and the models which are used in this paper are VGG 19 and RESSNET 18. We included facial emotional recognition with gender identification also. In this project we have used fer2013 and ck+ dataset and ultimately achieved 73% and 94% around accuracies respectively.

scholarly journals Facial expression recognition based on improved VGG convolutional neural network

2021 ◽  
Vol 2083 (3) ◽  
pp. 032030
Author(s):  
Cui Dong ◽  
Rongfu Wang ◽  
Yuanqin Hang
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Facial Expression ◽  
Facial Expression Recognition ◽  
Recognition Rate ◽  
Expression Recognition ◽  
Original Network ◽  
Feature Information ◽  
Fully Connected ◽  
A Current

Abstract With the development of artificial intelligence, facial expression recognition based on deep learning has become a current research hotspot. The article analyzes and improves the VGG16 network. First, the three fully connected layers of the original network are changed to two convolutional layers and one fully connected layer, which reduces the complexity of the network; Then change the maximum pooling in the network to local-based adaptive pooling to help the network select feature information that is more conducive to facial expression recognition, so that the network can be used on the facial expression datasets RAF-DB and SFEW. The recognition rate increased by 4.7% and 7% respectively.

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