scholarly journals Human Behavior Analysis through Facial Expression Recognition in Images using Deep Learning

2019 ◽  
Vol 9 (2) ◽  
pp. 391-397
Keyword(s):  
Deep Learning ◽  
Facial Expression ◽  
Facial Expression Recognition ◽  
Strong Relationship ◽  
Training Data ◽  
Expression Recognition ◽  
Head Pose ◽  
Network Algorithms ◽  
Sentiment Detection ◽  
Human Behavior Analysis

Facial Expression Recognition is an important undertaking for the machinery to recognize different expressive alterations in individual. Emotions have a strong relationship with our behavior. Human emotions are discrete reactions to inside or outside occasions which have some importance meaning. Involuntary sentiment detection is a process to understand the individual’s expressive state to identify his intensions from facial expression which is also a noteworthy piece of non-verbal correspondence. There are seven essential emotions which incorporate Cheerful, Gloomy, Anger, Terror, Astonish, Hatred as well as Unbiased. In the present period of HumanComputer Interaction (HCI), making machines to analyze and recognize emotions is a difficult task. Recent FER systems are lacking of sufficient training data and other problems like illumination and head pose to identify emotions. Inside this article, we provide a comprehensive learning of Facial expression detection with Deep Learning methods which includes different Neural Network Algorithms used with different datasets and its efficiency result. Also we will provide current challenges and current opportunities in this field to develop robust FER using Deep learning.

scholarly journals Ensemble of Multi Feature Layers in CNN for Facial Expression Recognition using Deep Learning

2019 ◽  
Vol 8 (4) ◽  
pp. 9782-9787
Keyword(s):  
Neural Networks ◽  
Facial Expression ◽  
Facial Expression Recognition ◽  
Recognition Accuracy ◽  
Strong Relationship ◽  
Expression Recognition ◽  
Final Layer ◽  
Japanese Female ◽  
Sentiment Detection ◽  
Fully Connected

Facial Expression Recognition is an important undertaking for the machinery to recognize different expressive alterations in individual. Emotions have a strong relationship with our behavior. Human emotions are discrete reactions to inside or outside occasions which have some importance meaning. Involuntary sentiment detection is a process to understand the individual’s expressive state to identify his intensions from facial expression which is also a noteworthy piece of non-verbal correspondence. In this paper we propose a Framework that combines discriminative features discovered using Convolutional Neural Networks (CNN) to enhance the performance and accuracy of Facial Expression Recognition. For this we have implemented Inception V3 pre-trained architecture of CNN and then applying concatenation of intermediate layer with final layer which is further passing through fully connected layer to perform classification. We have used JAFFE (Japanese Female Facial Expression) Dataset for this purpose and Experimental results show that our proposed method shows better performance and improve the recognition accuracy.

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 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 Real Time Facial Expression Recognition System Based on Deep Learning

2019 ◽  
Vol 8 (2S11) ◽  
pp. 4047-4051
Keyword(s):  
Deep Learning ◽  
Facial Expression ◽  
Real Time ◽  
Facial Expression Recognition ◽  
Recognition System ◽  
Expression Recognition ◽  
Or Education ◽  
Face Expression ◽  
Active Research ◽  
Main Components

The automatic detection of facial expressions is an active research topic, since its wide fields of applications in human-computer interaction, games, security or education. However, the latest studies have been made in controlled laboratory environments, which is not according to real world scenarios. For that reason, a real time Facial Expression Recognition System (FERS) is proposed in this paper, in which a deep learning approach is applied to enhance the detection of six basic emotions: happiness, sadness, anger, disgust, fear and surprise in a real-time video streaming. This system is composed of three main components: face detection, face preparation and face expression classification. The results of proposed FERS achieve a 65% of accuracy, trained over 35558 face images..

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