Emotion Detection and its Effect on Decision-making (original) (raw)
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Emotion Detection Using Machine Learning
International Journal of Recent Trends in Engineering and Research, 2017
The use of machines to perform different tasks is constantly increasing in society. Providing machines with perception can lead them to perform a great variety of tasks. Machine perception requires that machines understand about their environment. Recognizing facial emotions will thus help in this regard. We use the TensorFlow library and the Inception model and apply transfer-learning for our dataset to retrain the model. We then identify the facial emotions: happiness, sadness, anger and surprise. Keywords-deep-learning;deep-neural-networks;TensorFlow;Inception;transferlearning;convolutional neural networks. I.INTRODUCTION Facial expressions play a very important role in human communication. The human face is the richest source of emotions. As society continues to make more use of human-machine interactions, it is important for machines to be able to interpret facial expressions in order to improve their authenticity or to make them less machine and more human. Our brains make vision seem easy. It doesn't take any effort for humans to tell apart a lion and a tiger, read an article, or recognizing a human's face. But these are actually hard problems to solve with a computer: they only seem easy because our brains are incredibly good at understanding images. Emotion can be recognized through a variety of means such as voice intonation, body language, and more complex methods such electroencephalography (EEG). However, the easier, more practical method is to examine facial expressions. There are seven types of human emotions shown to be universally recognizable across different cultures: anger, disgust, fear, happiness, sadness, surprise, contempt. Interestingly, even for complex expressions where a mixture of emotions could be used as descriptors, cross-cultural agreement is still observed. Therefore, a utility that detects emotion from facial expressions would be widely applicable. Such advancement could bring applications in medicine, marketing and entertainment. The task of emotion recognition is particularly difficult for two reasons: (1) There does not exist a large database of training images and (2) classifying emotions can be difficult depending on whether the input image is static or in a transition frame into a facial expression. The latter issue is particularly difficult for real-time detection where facial expressions vary dynamically. In the last few years the field of machine learning has made tremendous progress on addressing these difficult problems. In particular, the discovery of the model called a deep convolutional neural network can achieve reasonable performance on hard visual recognition tasks matching or exceeding human performance in some domains. It is now being used by popular tech-giants, Facebook being one of the leading users. Researchers have demonstrated steady progress in computer vision by validating their work against ImageNet an academic benchmark for computer vision.
A Comprehensive Investigation on Emotional Detection in Deep Learning
International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2022
Emotion recognition is a substantial problem in the field of Big Data. In a wide range of applications, reliable categorization, analysis, and interpretation of emotional content is greatly desired. In this paper, we look at how Deep Learning models perform on an emotion perception test. Facial Expression Recognition (FER) plays an important role in machine learning tasks. Deep Learning models do well in FER tasks, but they lack explanation for their conclusions. Based on the notion that facial expression is a mixture of facial muscle movements, we discover a link between Facial Action Coding Units (AUs) and Emotion label in the CK+ Dataset. In this study, we offer a model that uses AUs to explain the classification outcomes of a Convolutional Neural Network (CNN) model. The CNN model is trained using the CK+ Dataset and identifies emotions using extracted characteristics. The CNN model's retrieved features and emotion classes are used by the explanation model to classify multiple AUs. Explanation model creates AUs quite effectively with only characteristics and emotion classes acquired from the CNN model, according to our trial. Experimental research was constructed, and several deep learning approaches were tested using publically available datasets. The findings are highly intriguing, highlighting the benefits of each strategy and training style studied.
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.
Machine learning for human emotion recognition: a comprehensive review
2024
Emotion is an interdisciplinary research field investigated by many research areas such as psychology, philosophy, computing, and others. Emotions influence how we make decisions, plan, reason, and deal with various aspects. Automated human emotion recognition (AHER) is a critical research topic in Computer Science. It can be applied in many applications such as marketing, human-robot interaction, electronic games, E-learning, and many more. It is essential for any application requiring to know the emotional state of the person and act accordingly. The automated methods for recognizing emotions use many modalities such as facial expressions, written text, speech, and various biosignals such as the electroencephalograph, blood volume pulse, electrocardiogram, and others to recognize emotions. The signals can be used individually(uni-modal) or as a combination of more than one modality (multi-modal). Most of the work presented is in laboratory experiments and personalized models. Recent research is concerned about in the wild experiments and creating generic models. This study presents a comprehensive review and an evaluation of the state-of-the-art methods for AHER employing machine learning from a computer science perspective and directions for future research work. Keywords Emotion recognition analysis Á Physical signals Á Intrusive and non-intrusive emotion recognition Á Physiological signals Á Facial expressions Á Speech stimuli Á Body postures and gestures Á Machine learning and deep learning techniques
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.
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.
Emotion Classification from Facial Images and Videos Using a Convolutional Neural Network
As a result of its wide range of academic and commercial applications, emotion recognition seems to be a important subject in computer vision & artificial intelligence. The majority of the decisions we make in our life are influenced by emotions. In this technology advancement, researchers found that properly categorising of human emotions may be a major source of development for companies in digital marketing. And that is what we will indeed be focusing on reading emotions of human being from facial image and videos. In the world of artificial intelligence, this concept falls under the category of cognitive systems. Facial expressions are essential to take into account while researching human behaviour including psychological characteristics. In this work, we used deep learning algorithms that recognise basic seven emotions through facial expressions (FER) and videos: happy, surprise, disgust, anger, neutral, sadness, and fear (VER). Deep learning has the potential to improve human-machine communication interaction because of its ability to learn features will allow machines to develop perception. To classify the emotions from facial images using deep learning techniques, we created the Convolution Neural Network Model and trained it on fer2013, a database of pre-recorded images with various emotions. And for emotion recognition from videos, we segment the video into individual frames at 30 frames per second and repeat the process of facial images on each frame, then do sentiment analysis, and finally reframe the emotional analysis output video with all of the available emotional individual frames.
Classification of Emotion Detection using Deep Neural Network
2020
Emotions are subjective, people would interpret it differently. It is hard to define the notion of emotions. Annotating an audio recording is challenging. We label a complete sentence. There are lots of audio data can be achieved from films or news therefore collection of data is complex. However, both of them are biased since news reporting has to be neutral and actors’ emotions are imitated. It is hard to look for neutral audio recording without any bias. Labelling data require high human and time cost. Unlike drawing a bounding box on an image, it requires trained personnel to listen to the whole audio recording, analysis it and give an annotation. The annotation result has to be evaluated by multiple individuals due to its subjectivity. In particular, we are presenting a classification model of emotions elicited by speeches based on deep neural networks (CNNs). For the purpose, we focused on the audio recordings available in the Ryerson Audio-Visual Database of Emotional Speech ...
International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2022
In this project emotion detection using its facial expressions are going to be detected. These expressions are often derived from the live feed via system's camera or any pre-existing image available within the memory. Emotions possessed by humans will be recognized and contains a vast scope of study within the computer vision industry upon which several research have already been done. The work has been implemented using Python (3.10), Open-Source Computer Vision Library (OpenCV) and NumPy. The run time video (testing dataset) is being compared to training dataset and thus emotion is predicted. the target of this paper is to develop a system which might analyze the image and run time video and predict the expression of the person. The study proves that this procedure is workable and produces valid results.in this project we've got make change to the accuracy of the running project by using the various models of python and deep learning.
Facial Emotion Detection Using Deep Learning
International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2023
The use of machines to perform various tasks is ever increasing in society. By imbuing machines with perception, they will be able to perform a wide variety of tasks. There are also very complex ones, such as aged care. Machine perception requires the machine to understand the surrounding environment and the intentions of the interlocutor. Recognizing facial emotions can help in this regard. During the development of this work, deep learning techniques were used on images showing facial emotions such as happiness, sadness, anger, surprise, disgust, and fear. In this study, a pure convolutional neural network approach outperformed the results of other statistical methods obtained by other authors, including feature engineering. The use of convolutional networks includes a learning function. This looks very promising for this task where the functionality is not easy to define. Additionally, the network he was evaluated using two different corpora. One was used during network training and also helped tune parameters and define the network architecture. This corpus consisted of mimetic emotions. The network that yielded the highest classification accuracy results was tested on the second dataset. Although the network was trained on only one corpus, the network reported promising results when tested on another dataset showing non-real facial emotions. The results achieved did not correspond to the state of the art. Collected evidence indicates that deep learning may be suitable for facial expression classification. Deep learning therefore has the potential to improve human-machine interaction. Because the ability to learn functions allows machines to evolve cognition. And through perception, the machine could offer a smoother response, greatly improving the user's experience.