Real Time Facial Expression Recognition using Convolution Neural Network Algorithm (original) (raw)

A Facial Expression Recognition System using Convolutional Neural Network

A Facial expression is the visible manifestation of the affective state, cognitive activity, intention, personality and psychopathology of a person and plays a communicative role in interpersonal relations. Automatic recognition of facial expressions can be an important component of natural human-machine interfaces; it may also be used in behavioral science and in clinical practice. An automatic Facial Expression Recognition system needs to perform detection and location of faces in a cluttered scene, facial feature extraction, and facial expression classification. Facial expression recognition system is implemented using Convolution Neural Network (CNN). CNN model of the project is based on LeNet Architecture. Kaggle facial expression dataset with seven facial expression labels as happy, sad, surprise, fear, anger, disgust, and neutral is used in this project. The system achieved 56.77 % accuracy and 0.57 precision on testing dataset.

Facial Expression Recognition Using Convolutional Neural Network

International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2022

Human & computer interaction has been an important field of study for ages. Humans share universal and fundamental set of emotions which are exhibited through consistent facial expressions or emotion. If computer could understand the feelings of humans, it can give the proper services based on the feedback received. An algorithm that performs detection, extraction, and evaluation of these facial expressions will allow for automatic recognition of human emotion in images and videos. Automatic recognition of facial expressions can be an important component of natural human-machine interfaces; it may also be used in behavioural science and in clinical practices. In this model we give the overview of the work done in the past related to Emotion Recognition using Facial expressions along with our approach towards solving the problem. The approaches used for facial expression include classifiers like Support Vector Machine (SVM), Convolution Neural Network (CNN) are used to classify emotions based on certain regions of interest on the face like lips, lower jaw, eyebrows, cheeks and many more. Kaggle facial expression dataset with seven facial expression labels as happy, sad, surprise, fear, anger, disgust, and neutral is used in this project. The system achieved 56.77 % accuracy and 0.57 precision on testing dataset.

Facial Expression Recognition Using Convolutional Network

International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2022

This paper focuses on Emotion Recognition using facial expressions. It aims to detect facial expressions accurately and efficiently. Convolutional Neural Network has developed to help in recognizing emotions through facial expressions and classify them into seven basic categories which are: happy, sad, neutral, surprise, fear, disgust, and angry. So, Convolutional Neural Network is implemented to extract relevant features of the input images and classify them into seven labels. For evaluating the proposed model, Facial Emotion Recognition 2013 dataset is used so that the model achieves the best accuracy rate. 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.

Real-time facial expression recognition using CNN

International Journal of Advance Research, Ideas and Innovations in Technology, 2020

Enhancing modern day machines or computers to recognize various facial expressions and to understand human emotions from them in real time is an exigent research subject. Through this paper, I put forward a solution to recognize emotions by understanding different facial expressions by collecting live video through a Flask App created. I deploy a Flask App to video stream live feed captured through the local camera attached to the machine or computer system. The video captured is fed to various image extraction techniques. The facial features are identified by different operations provided by OpenCV and the region consisting of parts of the face are made to surround or enclose by a contour. This region, enclosed by the contour is used as an input to Convolutional Neural Network (CNN). The CNN model created consists of six activation layers, of which four are convolution layers and two are fully controlled layers. Each layer is designed to undergo several training techniques. The main objective of this project is to demonstrate the accuracy of Convolutional Neural Network model designed. The paper is concluded by discussing the outcomes of our project and the ways to improve the efficiency of the model. The scope of this project is also analyzed to enhance technologies developed in the near future.

Emotion Recognition of Facial Expression Using Convolutional Neural Network

Innovative Data Communication Technologies and Application

Recognition of emotion using facial information is an interesting field for computer science, medicine, and psychology. Various researches are working with automated facial expression recognition system. Convolutional neural network (CNN) for facial emotion recognition method is basically used to recognize different-different human facial landmarks, geometrical poses, and emotions of faces. Human facial expression gives very important information to understand the emotions of a person for an interpersonal relationship. Since it is a classification problem, so the performance of any classifier is dependent on features extracted from the region of interest of the sample. In this paper, we are going to train the machine to recognize different types of emotions through human facial expressions using the Convolutional Neural Network (CNN). We have used sequential forward selection algorithms and softmax activation function.

CNN based Facial Expression Recognition System

Social Science Research Network, 2021

Training machines to think and behave like humans have fascinated many researchers around the world. Deep learning a subgroup of Machine Learning enables us to develop a system that can extract features, recognize distinct patterns, and classifies them just like a human brain. Facial Expression Recognition (FER) is one of the trending technology in the Human-Machine Interaction field. This paper contributes to a novel system that makes use of Convolutional Neural networks with fewer data samples and high accuracy. The main objective is to develop a system that can classify the different expressions from the human face and identifies them accurately. In our system, we had developed a 15 layer CNN architecture which plays an important role in classifying images and training the system. This existing system can classify 6 basics facial expressions such as happy, angry, fear, sad, disgust, and surprise with an accuracy of 98.1%.

Automatic Facial Expression Recognition using Convolutional Neural Network (CNN

Facial Expression Recognition is has been widely used in Artificial Intelligence, Human-Computer Interaction, and Security Monitoring. Convolution neural network (CNN) works as a depth learning architecture and it can extract the essential features of the image. In the case of large changes in shooting conditions, CNN's effect is better than the methods of Support Vector Machines (SVM) and Principal Component Analysis (PCA). Therefore, we are proposing a method based on CNN. The purpose is to classify each facial image as one of the seven facial expressions considered here. A new convolution neural network structure has been designed according to the characteristics of facial expression recognition. To extract implicit features convolution kernel is being used and max-pooling is being used to reduce the dimensions of the extracted implicit features. In comparison to AlexNet network, we can improve the recognition accuracy about on the FER and CK+ facial expression database with the help of Batch Normalization (BN) layer to our network. A facial expression recognition system is constructed for the convenience of application, and all the experimental results show that the system can reach the real-time needs.

FACIAL EMOTION RECOGNITION USING CONVOLUTION NEURAL NETWORK

Facial expression recognition is a very active research topic due to its potential applications in the many fields such as human-robot interaction, human-machine interfaces, driving safety, and health-care. Despite of the significant improvements, facial expression recognition is still a challenging problem that wait for more and more accurate algorithms. This article presents a new model that is capable of recognizing facial expression by using deep Convolutional Neural Network (CNN). The CNN model is generated by using Caffe in Digits environment. Moreover, it is trained and tested on NVIDIA Tegra TX1 embedded development platform including a 250 Graphics Processing Unit (GPU) CUDA cores and Quadcore ARM Cortex A57 processor. The proposed model is applied to address the facial expression problem on the publicly available two expression databases, the JAFFE database and the Cohn-Kanade database.

Emotion Recognition through Facial Expression Using CNN

International Journal of Computer Science and Information Security (IJCSIS), Vol. 21, No. 7, July, 2023

In recent past years, facial emotion recognition has been widely used in many applications and fields such as security, banking, marketing, and even in daily life applications. People usually refer to their mode or psychological states through emotions. There are many attempts has been focused on facial emotion recognition using deep learning techniques such as convolutional neural network (CNN). In this paper, propose a novel model to detect the changes of emotions of people, based on CNN with some popular libraries such as TensorFlow and Keras. The experimental results of the proposed model show the model performance in term of accuracy achieved 99.59%.

Real Time Emotion Recognition from Facial Expressions Using CNN Architecture

Emotion is an important topic in different fields such as biomedical engineering, psychology, neuroscience and health. Emotion recognition could be useful for diagnosis of brain and psychological disorders. In recent years, deep learning has progressed much in the field of image classification. In this study, we proposed a Convolutional Neural Network (CNN) based LeNet architecture for facial expression recognition. First of all, we merged 3 datasets (JAFFE, KDEF and our custom dataset). Then we trained our LeNet architecture for emotion states classification. In this study, we achieved accuracy of 96.43% and validation accuracy of 91.81% for classification of 7 different emotions through facial expressions.