Emotion Recognition (original) (raw)

Emotion Detection Using Machine Learning in Python

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

In this project emotion detection using its facial expressions will be detected. It can be derived from the live feed via system's camera or any pre-existing image available in the memory. Emotions possessed by humans can be detect by machine and has a vast scope of study in the computer vision industry upon which several research have already been done. The work has been implemented using Anaconda (Jupyter Notebook) (3.10), Open-Source Computer Vision Library (OpenCV) and NumPy. The code check the video (testing dataset) is being compared to training dataset and thus emotion is predicted. The objective of this paper is to develop a system which can analyze the image and run time video and predict the expression of the person. The study proves that this project and code is workable and produces valid results.in this project we have make change to the accuracy of the running project by using the different models of python and deep learning.

Facial Emotion Recognition and Detection in Python Using Deep Learning

2021

Human facial emotion recognition (FER) has attracted the eye of the research network for its promising applications. Mapping one of a kind facial expressions to the respective emotional states are the primary task in FER. The classical FER consists of two most important steps: feature extraction and emotion recognition. presently, the Deep Neural Networks, particularly the Convolutional Neural network (CNN), is extensively used in FER with the aid of distinctive feature of its inherent feature extraction mechanism from pictures. numerous works were reported on CNN with only some layers to clear up FER issues. but, wellknown shallow CNNs with straightforward getting to know schemes have restricted characteristic extraction capability to seize emotion data from high-resolution pictures. A notable disadvantage of the most current techniques is that they consider only the frontal pictures (i.e., ignore profile perspectives for convenience), despite the fact that the profile perspectives taken from different angles are essential for a practical FER system. For growing a highly correct FER system, this study proposes a completely Deep CNN (DCNN) modeling thru transfer learning (TL) technique wherein a pre-skilled DCNN model is followed through changing its dense top layer(s) well suited with FER, and the model is great-tuned with facial emotion data. a novel pipeline strategy is brought, wherein the training of the dense layer(s) is accompanied via tuning each of the pre-skilled DCNN blocks successively that has brought about gradual improvement of the accuracy of FER to a better level.

Emotion Detection using Image Processing in Python

ArXiv, 2020

In this work, user's emotion using its facial expressions will be detected. These expressions can be derived from the live feed via system's camera or any pre-exisiting image available in the memory. Emotions possessed by humans can be recognized and has a vast scope of study in the computer vision industry upon which several researches have already been done. The work has been implemented using Python (2.7, Open Source Computer Vision Library (OpenCV) and NumPy. The scanned image(testing dataset) is being compared to the training dataset and thus emotion is predicted. The objective of this paper is to develop a system which can analyze the image and predict the expression of the person. The study proves that this procedure is workable and produces valid results.

Facial Emotion Recognition Using Deep Neural Network

International Journal of Innovative Research in Engineering and Management (IJIREM), 2023

The major part in the process of humanization of systems is the capability of distinguishing the emotions of the person. In this research paper we represent the composition of an instinctive system that is capable of detecting the emotion by using their facial expressions. Three techniques of neural network are tailored, educated and subordinated to different jobs, after this the performance of the network was improved. A live videotape operation that can currently simulate the person’s emotion depicts how well the model connects to the world. Since the invention of computers many technologists and masterminds are introducing instinctively intelligent systems that are very helpful to humans mentally and physically. In the previous decades the usage of computer has increased rapidly which helps in developing fast literacy systems, where internet has provided vast quantum of data for teaching the machine. These two enlargements elevated the exploration on intelligent learning systems by using neural networks in favorable ways. The facial emotion detection machine needs to be trained to get the system ready. The installation of OpenCV(Open Source Computer Vision) is essential for this machine. OpenCV is a library that is required for computer vision.

A Framework for Emotion Detection using Open Source Computer Vision and Convolutional Neural Network

The Human emotions are the mental states of sentiments happen without conscious effort, and accompanied by physiological changes in facial muscles that result in facial expressions. The human-computer interaction uses nonverbal communication methods to know the emotion of a person through facial expressions, eye movement, and gestures. Besides, facial expression is a common procedure to find the mood because it transmits people's emotional states and feelings. Emotion recognition is a difficult task due to the facial patterns variety and complexity appears over face. The traditional computational methods fail to predict the emotions due to the variety and complexity of facial patterns. Therefore, machine learning methods are deployed to find better result for emotions detection. In this work, we have developed a model by using convolution neural networks, Open source computer vision, and tensorflow to detect the emotions of a person. Seven different emotions like anger, neutral, disgust, fear, happiness, sadness, and surprise are proposed by the model through the posture of mouth and eyes of a person.

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.

Emotional Analysis using Deep Learning

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2023

Emotions are mental states that accompany physiological changes in the face, resulting in facial expressions. Sympathy, anger, worry, joy, fright, and other significant emotions are a few examples. Facial expressions play a significant role in non-verbal communication because they encapsulate a person's emotions. There has been a great deal of research done on computer modelling of human emotions. Computer modelling of human emotions has been made possible by computer technology. However, it is still in its infancy. The authors attempted to overcome limitations and create new opportunities as well as gain a better understanding and implement this simple form of human interaction in proposed computer-aided world. It has been made possible to evaluate and interpret genuine facial expressions in real time thanks to new techniques for collecting facial expressions and quick, highresolution pictures. The FER (Facial Expression Recognition) method currently relies on motionless frames, which makes it very hard to recognize foreground from background in the absence of motion information. This study describes a real-time facial expression identification system that detects faces using HAAR cascading classification and classifies facial expressions using convolutional neural networks. The system utilizes a webcam to dynamically display emotion text and accurately categorizes seven major emotions, including anger, disgust, fear, happiness, sadness, surprise, and neutrality. Real-time facial expression recognition may be utilised in a number of real-world applications, including as airport security, trade, and medical monitoring.

A Review on Facial Emotion Recognition and Classification Analysis with Deep Learning

Biochemical and Biophysical Research Communications, 2021

Automatic face expression recognition is an exigent research subject and a challenge in computer vision. It is an interdisciplinary domain standing at the crossing of behavioural science, psychology, neurology, and artificial intelligence. Human-robot interaction is getting more significant with the automation of every field, like treating autistic patients, child therapy, babysitting, etc. In all the cases robots need to understand the present state of mind for better decision making. It is difficult for machine learning techniques to recognize the expressions of people since there will be significant changes in the way of their expressions. The emotions expressed through the human face have its importance in making arguments and decisions on different subjects. Machine Learning with Computer Vision and Deep Learning can be used to recognize facial expressions from the preloaded or real time images with human faces. DNN (Deep Neural Networking) is one among the hottest areas of research and is found to be very effective in classification of images with a high degree of accuracy. In the proposed work, the popular dataset CK+ is analysed for comparison. The dataset FER 2013 and home-brewed data sets are used in the work for calculating the accuracy of the model created. The results are obtained in such a way that DCNN approach is very efficient in facial emotion recognition. Experiments and study show that the dataset, FER 2013 is a high-quality dataset with equal efficiency as the other two popular datasets. This paper aims to ameliorate the accuracy of classification of facial emotion.

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...

Deep Learning on Facial Expression Detection : Artificial Neural Network Model Implementation

CCIT Journal

The moods, emotions, and even medical issues of a person can frequently be seen directly reflected in their facial expressions. The fields of social science and human-computer interaction have recently begun to pay more attention to facial emotion detection as a result of this. The primary focus of this study is on the automatic recognition of human facial expressions using an artificial neural network (ANN) model and a technique based on straightforward convolution. The dataset utilized is a self-mined dataset that was obtained by utilizing the web scraping approach on Google Image with the help of the Selenium package for Python. A dataset containing six categories of fundamental human expressions that are likely to be met on a daily basis, namely anger, confusion, contempt, crying, sadness, disgust, and happiness, with a total of 6,016 photos being used. The goal of this research is to determine how accurate the model of artificial neural networks can be in predicting.