FACIAL EMOTION RECOGNITION USING CONVOLUTION NEURAL NETWORK (original) (raw)

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

Optimizing Deep Convolutional Neural Network for Facial Expression Recognitions

Advances in intelligent systems and computing, 2018

Facial expression recognition (FER) systems have attracted much research interest in the area of Machine Learning. We designed a large, deep convolutional neural network to classify 40,000 images in the data-set into one of seven categories (disgust, fear, happy, angry, sad, neutral, surprise). In this project, we have designed deep learning Convolution Neural Network (CNN) for facial expression recognition and developed model in Theano and Caffe for training process. The proposed architecture achieves 61% accuracy. This work presents results of accelerated implementation of the CNN GPUs. Optimizing Deep CNN is to reduce training time for system.

Optimizing Deep Convolutional Neural Network for Facial Expression Recognition

European Journal of Engineering Research and Science

Facial expression recognition (FER) systems have attracted much research interest in the area of Machine Learning. We designed a large, deep convolutional neural network to classify 40,000 images in the data-set into one of seven categories (disgust, fear, happy, angry, sad, neutral, surprise). In this project, we have designed deep learning Convolution Neural Network (CNN) for facial expression recognition and developed model in Theano and Caffe for training process. The proposed architecture achieves 61% accuracy. This work presents results of accelerated implementation of the CNN with graphic processing units (GPUs). Optimizing Deep CNN is to reduce training time for system.

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.

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%.

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 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.

Facial Expression Recognition Based on Deep Learning Convolution Neural Network: A Review

Journal of Soft Computing and Data Mining, 2021

Facial emotional processing is one of the most important activities in effective calculations, engagement with people and computers, machine vision, video game testing, and consumer research. Facial expressions are a form of nonverbal communication, as they reveal a person's inner feelings and emotions. Extensive attention to Facial Expression Recognition (FER) has recently been received as facial expressions are considered. As the fastest communication medium of any kind of information. Facial expression recognition gives a better understanding of a person's thoughts or views and analyzes them with the currently trending deep learning methods. Accuracy rate sharply compared to traditional state-of-the-art systems. This article provides a brief overview of the different FER fields of application and publicly accessible databases used in FER and studies the latest and current reviews in FER using Convolution Neural Network (CNN) algorithms. Finally, it is observed that everyo...

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.