scholarly journals A Facial Expressions Recognition Method Using Residual Network Architecture for Online Learning Evaluation

2021 ◽  
Vol 25 (6) ◽  
pp. 953-962
Author(s):  
Duong Thang Long ◽  
Keyword(s):  
Online Learning ◽  
Facial Expressions ◽  
Network Architecture ◽  
Integrated System ◽  
Training Data ◽  
Expression Recognition ◽  
Residual Network ◽  
Practical Applications ◽  
Proposed Model ◽  
Day By Day

Facial expression recognition (FER) has been widely researched in recent years, with successful applications in a range of domains such as monitoring and warning of drivers for safety, surveillance, and recording customer satisfaction. However, FER is still challenging due to the diversity of people with the same facial expressions. Currently, researchers mainly approach this problem based on convolutional neural networks (CNN) in combination with architectures such as AlexNet, VGGNet, GoogleNet, ResNet, SENet. Although the FER results of these models are getting better day by day due to the constant evolution of these architectures, there is still room for improvement, especially in practical applications. In this study, we propose a CNN-based model using a residual network architecture for FER problems. We also augment images to create a diversity of training data to improve the recognition results of the model and avoid overfitting. Utilizing this model, this study proposes an integrated system for learning management systems to identify students and evaluate online learning processes. We run experiments on different datasets that have been published for research: CK+, Oulu-CASIA, JAFFE, and collected images from our students (FERS21). Our experimental results indicate that the proposed model performs FER with a significantly higher accuracy compared with other existing methods.

Concise CNN model for face expression recognition

2021 ◽  
pp. 1-9
Author(s):  
Harshadkumar B. Prajapati ◽  
Ankit S. Vyas ◽  
Vipul K. Dabhi
Keyword(s):  
Network Architecture ◽  
Expression Recognition ◽  
Human Machine Interaction ◽  
Face Expression ◽  
Computational Overhead ◽  
Average Accuracy ◽  
Proposed Model ◽  
Face Expression Recognition ◽  
Expression Classification ◽  
Machine Interaction

Face expression recognition (FER) has gained very much attraction to researchers in the field of computer vision because of its major usefulness in security, robotics, and HMI (Human-Machine Interaction) systems. We propose a CNN (Convolutional Neural Network) architecture to address FER. To show the effectiveness of the proposed model, we evaluate the performance of the model on JAFFE dataset. We derive a concise CNN architecture to address the issue of expression classification. Objective of various experiments is to achieve convincing performance by reducing computational overhead. The proposed CNN model is very compact as compared to other state-of-the-art models. We could achieve highest accuracy of 97.10% and average accuracy of 90.43% for top 10 best runs without any pre-processing methods applied, which justifies the effectiveness of our model. Furthermore, we have also included visualization of CNN layers to observe the learning of CNN.

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 Memory Model for Morphological Semantics of Visual Stimuli Using Sparse Distributed Representation

2021 ◽  
Vol 11 (22) ◽  
pp. 10786
Author(s):  
Kyuchang Kang ◽  
Changseok Bae
Keyword(s):  
Neural Networks ◽  
Visual Stimuli ◽  
Training Data ◽  
Memory Model ◽  
Distributed Representation ◽  
Data Sets ◽  
Memory Process ◽  
Practical Applications ◽  
Proposed Model ◽  
Continual Learning

Recent achievements on CNN (convolutional neural networks) and DNN (deep neural networks) researches provide a lot of practical applications on computer vision area. However, these approaches require construction of huge size of training data for learning process. This paper tries to find a way for continual learning which does not require prior high-cost training data construction by imitating a biological memory model. We employ SDR (sparse distributed representation) for information processing and semantic memory model, which is known as a representation model of firing patterns on neurons in neocortex area. This paper proposes a novel memory model to reflect remembrance of morphological semantics of visual input stimuli. The proposed memory model considers both memory process and recall process separately. First, memory process converts input visual stimuli to sparse distributed representation, and in this process, morphological semantic of input visual stimuli can be preserved. Next, recall process can be considered by comparing sparse distributed representation of new input visual stimulus and remembered sparse distributed representations. Superposition of sparse distributed representation is used to measure similarities. Experimental results using 10,000 images in MNIST (Modified National Institute of Standards and Technology) and Fashion-MNIST data sets show that the sparse distributed representation of the proposed model efficiently keeps morphological semantic of the input visual stimuli.

scholarly journals System for Face Recognition under Different Facial Expressions Using a New Associative Hybrid Model Amαβ-KNN for People with Visual Impairment or Prosopagnosia

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 578 ◽  
Author(s):  
Moisés Márquez-Olivera ◽  
Antonio-Gustavo Juárez-Gracia ◽  
Viridiana Hernández-Herrera ◽  
Amadeo-José Argüelles-Cruz ◽  
Itzamá López-Yáñez
Keyword(s):  
Face Recognition ◽  
Facial Expressions ◽  
Hybrid Model ◽  
Integrated System ◽  
K Nearest Neighbors ◽  
The People ◽  
Proposed Model ◽  
The Face ◽  
Controlled Environments ◽  
The Individual

Face recognition is a natural skill that a child performs from the first days of life; unfortunately, there are people with visual or neurological problems that prevent the individual from performing the process visually. This work describes a system that integrates Artificial Intelligence which learns the face of the people with whom the user interacts daily. During the study we propose a new hybrid model of Alpha-Beta Associative memories (Amαβ) with Correlation Matrix (CM) and K-Nearest Neighbors (KNN), where the Amαβ-CMKNN was trained with characteristic biometric vectors generated from images of faces from people who present different facial expressions such as happiness, surprise, anger and sadness. To test the performance of the hybrid model, two experiments that differ in the selection of parameters that characterize the face are conducted. The performance of the proposed model was tested in the databases CK+, CAS-PEAL-R1 and Face-MECS (own), which test the Amαβ-CMKNN with faces of subjects of both sexes, different races, facial expressions, poses and environmental conditions. The hybrid model was able to remember 100% of all the faces learned during their training, while in the test in which faces are presented that have variations with respect to those learned the results range from 95.05% in controlled environments and 86.48% in real environments using the proposed integrated system.

Expressive Personalized 3D Face Models from 3D Face Scans

2020 ◽  
Author(s):  
Stella Graßhof
Keyword(s):  
3D Reconstruction ◽  
Facial Expressions ◽  
Quality Criteria ◽  
Training Data ◽  
3D Face ◽  
Proposed Model ◽  
Objective Quality ◽  
3D Scans ◽  
Face Synthesis ◽  
Expression Transfer

In this work, different methods are presented to create 3D face models from databases of 3D face scans. The challenge in this endeavour is to balance the limited training data with the high demands of various applications. The 3D scans stem from various persons showing different expressions, with varying number of points per 3D scan and different numbers of scans per person. This data of posed facial expressions revealed substructures, which are utilised to improve the proposed model. In the process of creating and using the models, for each specifc application objective quality criteria are carefully designed tailored to the task to quantify the quality. In total four face models built from three databases are compared based on: 3D face synthesis, 3D approximation, person and expression transfer, and 3D reconstruction from 2D. Contents Abbreviations and Nomenclature XII 1 Introduction 1 1.1 The Difficulty of Quality Assessment . . . . . . . . . . . . . . 2 1.2 Face Models ....

scholarly journals Convolutional Neural Network With Batch Normalization for Classification of Emotional Expressions Based on Facial Images

2021 ◽  
Vol 21 (1) ◽  
pp. 197-204
Author(s):  
Bambang Krismono Triwijoyo ◽  
Ahmat Adil ◽  
Anthony Anggrawan
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Intelligent Systems ◽  
Training Data ◽  
Emotional Expressions ◽  
Expression Recognition ◽  
Batch Normalization ◽  
Proposed Model ◽  
Accuracy Level ◽  
Facial Images

Emotion recognition through facial images is one of the most challenging topics in human psychological interactions with machines. Along with advances in robotics, computer graphics, and computer vision, research on facial expression recognition is an important part of intelligent systems technology for interactive human interfaces where each person may have different emotional expressions, making it difficult to classify facial expressions and requires training data. large, so the deep learning approach is an alternative solution., The purpose of this study is to propose a different Convolutional Neural Network (CNN) model architecture with batch normalization consisting of three layers of multiple convolution layers with a simpler architectural model for the recognition of emotional expressions based on human facial images in the FER2013 dataset from Kaggle. The experimental results show that the training accuracy level reaches 98%, but there is still overfitting where the validation accuracy level is still 62%. The proposed model has better performance than the model without using batch normalization.

scholarly journals A deep learning model for classifying human facial expressions from infrared thermal images

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ankan Bhattacharyya ◽  
Somnath Chatterjee ◽  
Shibaprasad Sen ◽  
Aleksandr Sinitca ◽  
Dmitrii Kaplun ◽  
...  
Keyword(s):  
Deep Learning ◽  
Facial Expressions ◽  
Building Blocks ◽  
Learning Model ◽  
Cognitive Responses ◽  
Expression Recognition ◽  
Infrared Thermal Imaging ◽  
Thermal Images ◽  
Proposed Model ◽  
Deep Learning Model

AbstractThe analysis of human facial expressions from the thermal images captured by the Infrared Thermal Imaging (IRTI) cameras has recently gained importance compared to images captured by the standard cameras using light having a wavelength in the visible spectrum. It is because infrared cameras work well in low-light conditions and also infrared spectrum captures thermal distribution that is very useful for building systems like Robot interaction systems, quantifying the cognitive responses from facial expressions, disease control, etc. In this paper, a deep learning model called IRFacExNet (InfraRed Facial Expression Network) has been proposed for facial expression recognition (FER) from infrared images. It utilizes two building blocks namely Residual unit and Transformation unit which extract dominant features from the input images specific to the expressions. The extracted features help to detect the emotion of the subjects in consideration accurately. The Snapshot ensemble technique is adopted with a Cosine annealing learning rate scheduler to improve the overall performance. The performance of the proposed model has been evaluated on a publicly available dataset, namely IRDatabase developed by RWTH Aachen University. The facial expressions present in the dataset are Fear, Anger, Contempt, Disgust, Happy, Neutral, Sad, and Surprise. The proposed model produces 88.43% recognition accuracy, better than some state-of-the-art methods considered here for comparison. Our model provides a robust framework for the detection of accurate expression in the absence of visible light.

Determining Subcategories of Facial Expressions for Improved Performance in Practical Applications

2020 ◽  
Vol 19 (01) ◽  
pp. 2050004
Author(s):  
Xuejian Wang ◽  
Michael C. Fairhurst ◽  
Anne M. P. Canuto
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Facial Expression Recognition ◽  
Expression Recognition ◽  
The Novel ◽  
Practical Applications ◽  
Improved Performance ◽  
Key Dimensions ◽  
Theories Of Emotions

In the context of facial expression recognition (FER), this paper reviews the fundamental theories of emotions and further explains the key dimensions of a defined emotional space. The main contribution of this paper is to propose a set of novel categorization methods for facial expressions to be used in the design of an automatic FER system. This novel categorization enables the facial expression to be interpreted in a better way that and to be more effective in practical applications of automatic FER systems. In order to validate the feasibility of the proposed categorization methods, a set of experiments is reported which investigates and analyzes the influence that the novel categorization brings to a multi-view FER system.

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

scholarly journals Automated Ventricular System Segmentation in Paediatric Patients Treated for Hydrocephalus Using Deep Learning Methods

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Michał Klimont ◽  
Mateusz Flieger ◽  
Jacek Rzeszutek ◽  
Joanna Stachera ◽  
Aleksandra Zakrzewska ◽  
...  
Keyword(s):  
Neural Network ◽  
Data Augmentation ◽  
Ct Images ◽  
Policy Transfer ◽  
Training Data ◽  
Intraobserver Variability ◽  
Practical Applications ◽  
Brain Scans ◽  
Rate Policy ◽  
Ct Brain

Hydrocephalus is a common neurological condition that can have traumatic ramifications and can be lethal without treatment. Nowadays, during therapy radiologists have to spend a vast amount of time assessing the volume of cerebrospinal fluid (CSF) by manual segmentation on Computed Tomography (CT) images. Further, some of the segmentations are prone to radiologist bias and high intraobserver variability. To improve this, researchers are exploring methods to automate the process, which would enable faster and more unbiased results. In this study, we propose the application of U-Net convolutional neural network in order to automatically segment CT brain scans for location of CSF. U-Net is a neural network that has proven to be successful for various interdisciplinary segmentation tasks. We optimised training using state of the art methods, including “1cycle” learning rate policy, transfer learning, generalized dice loss function, mixed float precision, self-attention, and data augmentation. Even though the study was performed using a limited amount of data (80 CT images), our experiment has shown near human-level performance. We managed to achieve a 0.917 mean dice score with 0.0352 standard deviation on cross validation across the training data and a 0.9506 mean dice score on a separate test set. To our knowledge, these results are better than any known method for CSF segmentation in hydrocephalic patients, and thus, it is promising for potential practical applications.

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