Real-time facial expression recognition using CNN (original) (raw)

Real Time Facial Expression Recognition using Convolution Neural Network Algorithm

International Journal for Research in Applied Science and Engineering Technology IJRASET, 2020

The most expressive way human beings display emotions is through facial expressions. The task of detecting facial expression of a human being via a computer is a very complex process due to its variability present across human faces including color, expression, position, and orientation. The aim of this paper is to presents a Convolution Neural Network (CNN) architecture for real-time facial expression recognition. We have used ICML 2013 Facial Expression Recognition Challenge dataset for this study and then trained our neural network for emotion state classification. In this study, we achieved accuracy of 84.18% and validation accuracy of 67.56% for classification of seven different emotions through facial expressions.

Facial Expression Recognition Using Python Using CNN Model

Current Journal of Applied Science and Technology

Facial expressions are a vital part of human life. Each day has a number of instances and all instances include numerous amounts of communication. Every communication expressed with emotion tells us about the state of the person. The interpersonal as well as security purposes are solved through facial expressions. The mischievous intention of a person can be caught by his expressions. The human mind can capture visual information faster. So, a machine recognizing it will be a challenge. As the saying goes- “A picture is worth a thousand words”- only when it is represented well. A machine being able to detect the atmosphere by the means of expression is less of a manual work. This paper detects the faces, extract the features as well classify them into different categories which ultimately lead to expression recognition. We evaluate our proposed method with the dataset which we used and the recall of angry, fear, happy, neutral, sad, and surprise is 60%, 31%, 84%, 22%, 57% and 58% re...

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%.

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 Deep Convolution Neural Network With Tensorflow

International Journal of Advance Research and Innovative Ideas in Education, 2020

Facial expression recognition (FER) is an automatic system that manipulates the facial data and plays a vital role in human machine interfaces. Olden machine learning algorithms has attracted incrementing attention from researchers since the early nineties. It approaches often requires complex feature extraction process. In this paper we reflected recent advances in deep learning to Convolutional Neural Networks (CNN). It is an prominent field which uses nowadays applications such as in robots, games and neuromarketing. It is widely used technique uses facial expressions, eye movement and gestures which conveys the emotional status and feelings of persons. The proposed model made pivot on detecting the facial expressions of an individual from a single image. The number of parameters in our proposed networks concentrated by decreasing manner that accelerates the total performance speed and makes this well and make suitable for real time systems

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.

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%.

Implementing a Real-time Emotion Detection System using Convolutional Neural Network

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

Humans express their emotions through their facial expressions. They are crucial in interpersonal communication. Every human mood can be documented using this project's real-time emotion recognition system. In this project, various models generated using machine learning and deep learning algorithms are used. To identify human expressions in real time, application software is created that makes use of a few powerful Python packages. This project makes use of a number of libraries, including Keras, OpenCV, and Matplotlib. Training those emotions can detect various human emotions. This real-time emotion detection can be used across multiple platforms.

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.

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.